M .U58 ... ....

I NO. 126 - ^

'

1980 ^

• . NBS BUILDING SCIENCE SERIES 126

Geographical Extrapolation of

Typical Hourly Weather Data for

Energy Calculation in Buildings

U.S. DEPARTMENT OF COMMERCE • NATIONAL BUREAU OF STANDARDS
NATIONAL BUREAU OF STANDARDS

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NBS BUILDING SCIENCE SERIES 126 , National Bureau o1 Suwirm

Library, EOl Afjmin. BIdg.

AUG 2 7 1980

Geographical Extrapolation of ^^v^r^'"^

Typical Hourly Weather Data for , us?

Energy Calculation in Buildings ^o. i^^

Edward A. Arensi

Larry E. Flynni

Daniel N. Nall2

Kalev Rubergi

^Center for Building Technology

National Engineering Laboratory

National Bureau of Standards

Washington, D.C. 20234

^Berkeley Solar Group

3026 Shattuck Avenue

Berkeley, CA 94705

U.S. DEPARTMENT OF COMMERCE, Philip M. Klutznick, Secretary

Luther H. Hodges, Jr., Deputy Secretary

Jordan J. Baruch, Assistant Secretary for Productivity, Technology and Innovation

NATIONAL BUREAU OF STANDARDS, Ernest Ambler, Director

Issued August 1980
Library of Congress Catalog Card Number: 80-600059

National Bureau of Standards Building Science Series 126

Nat. Bur. Stand. (U.S.), Bldg. Sci. Ser. 126, 121 pages (Aug. 1980)

CODEN; BSSNBV

U.S. GOVERNMENT PRINTING OFFICE

WASHINGTON: 1980

For sale by the Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402

Price $4.50

(Add 25 percent for other than U.S. mailing)
ACKNOWLEDGMENTS

The work described in this report was supported by the Department of

Energy under the Building Energy Conservation Criteria Program, Task

Order A008-BCS of Interagency Agreement No. EA-77-A-01-6010 . The

authors thank Tracey Kistler for preparing the manuscript, and

Tom Pierce and William Carroll for their helpful comments.

iii
TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS iii

ABSTRACT v CONVERSION FACTORS FROM METRIC TO ENGLISH UNITS vi

1. INTRODUCTION 1

2. METHOD 5

2.1 Sources of Monthly Summarized Data 5

2.2 Types of Sjmthetic Weather Data 6

2.3 Segment Selection Technique 7

2.3.1 Reasons for Selecting Unmodified Data 7 2.3.2 Selection Algorithm 8

2.3.3 Implementation of the Algorithm 10

2.3.4 Use of the Program SELECT 13

2.4 Hour-by-Hour Adjustment Technique 14

2.4.1 Reasons for Hour-by-Hour Adjustment 14

2.4.2 Adjustment Procedure 15

2.4.3 Use of the Program ADJUST 20

3. TESTS AND RESULTS 21

3.1 Test Program 21

3.2 Comparison of Weather Data Between Stations 24

3.3 Results 40

3.3.1 Representative Years Created by SELECT 40

3.3.2 Geographical Extrapolation Using SELECT 41

3.3.3 Geographical Extrapolation Using ADJUST 47

3.3.4 Geographical Extrapolation Using Both SELECT

and ADJUST 51

4. SYNTHESIZING CLIMATE DATA: CAVEATS 53

5. CONCLUSIONS 57

REFERENCES 61

APPENDIX A SELECT Listing Al

B ADJUST Listing Bl

C Subroutine Listings CI

D Example run of SELECT Dl

E Example run of ADJUST El

iv
ABSTRACT

Two techniques are developed and tested for creating composite and synthetic hourly weather data for a wide range of sites. The first technique selects real weather data segments from a source raultiyear weather

record, and links them into a composite synthetic year, in which the

hourly values are unchanged from the source. The second technique

adjusts the real hourly data values of the source to create a more

completely synthetic year. The techniques may be applied individually

or in combination. The resulting synthetic year or years can be used

to provide data that is representative of long-term climate for building

energy prediction either at the first-order station where the source hourly weather data were recorded, or at a nearby second-order station

for which only summarized climate averages are available. Additionally,

the adjustment technique can generate synthetic data to represent specific time periods at second-order stations for use in energy audits and

experiments. The effectiveness of extrapolating weather data from one location to another is assessed, and the uses of the two techniques are described. Two user-interactive Fortran programs, SELECT and ADJUST, are appended.

Key words: Building energy; computerized climate data.

V
CONVERSION FACTORS FROM METRIC (SI) TO ENGLISH UNITS

r nyslcai

Characteristic

To Convert from io Multiply by

m 1. L J •

Area m2 ft2 10.76

Velocity m/s mph 2.24

Temperature "C °F t-p = 1.8t(, + 32

Temperature

ajLX i. CLcIlL-C Op

r J.1 . 8o

Energy J Btu 0.948 X

10"^

Power W Btu/hr 3.41

U-value W/m^^C Btu/hr'ft2*°F 0.176

Thermal

Resistance m2°C/W hr'ft^'^F/Btu 5.678

Pressure kPa in. Hg 0.296

1 Giga joule (GJ) = lO^J = 0.948 Btu X 10^

Cover photo:

Average weather data from a station

near the buildng site may be used to

extrapolate more remotely-recorded

hourly weather data to the vicinity

of the building site.

Facing page:

Dulles International Airport f Virginia, Airports are usually the site

for recording hourly weather data,

vi
1 . INTRODUCTION

It is becoming common practice for building designers to analyze

building energy usage with computer programs that calculate the

building's thermal response for each hour of a complete year. The

results of these computations are used to evaluate alternative building

design and mechanical equipment schemes, to establish compliance with

codes, and to predict operating costs.

These programs require complete description of the building and hourly

observations of weather conditions for the building's location.

1
Magnetic tapes with recorded weather values for various locations are available from the National Climatic Center of the National Oceanic and

Atmospheric Administration (NOAA). Data recorded on these tapes include

dry- and wet-bulb temperatures, cloud cover and type, wind speed and

direction, solar radiation in some cases, barometric pressure, and

weather type. Using each hourly set of these variables in sequence,

the hour-by-hour program calculates the heat fluxes in the building.

In order to calculate the energy requirements for an annual cycle,

the program must perform 8,760 separate sets of calculations.

This report addresses two problems with hourly weather data for energy

calculation. The first problem is that hourly data are collected in

only a limited number of locations in the U.S. The number of Weather

Bureau and military stations collecting hourly or three-hourly data is

roughly 450. Since potential building sites could exist in any part of

the country, and climate varies regionally, there are numerous large

regions within which no hourly data is available to represent a building

location. If hourly data were used from a station outside the region

considerable inaccuracies could result. Few studies have been done that

suggest energy variability within moderately sized geographical regions.

Using temperature degree day values published by NOAA [1] as an indicator of energy use, one may predict a variability of 19 percent in annual

heating and 34 percent in annual cooling between Washington National

Airport and Dulles International Airport, about 25 miles apart in very

moderate terrain. Over rougher terrain and greater distances, one may

find considerably greater variability. In addition, differences in climate variables other than temperature, such as wind and sunlight, may

increase the variability. Within most geographic regions, computed

energy requirements will not reflect this variability because there is an insufficient number of stations recording hourly data. There is,

however, a much larger number of second-order stations recording monthly

summed averages of a fewer number of climate variables. If these monthly

averages could be utilized to generate hour-by-hour data for these additional locations, the prediction of building energy use in their vicinities could be improved.

The second problem is to find weather data that best predict the future

energy consumption of the building under design. The best estimate of

future climate over a period as long as the life cycle of a building is

the record of a long period in the immediate past, the thirty year

summarized normals published by NOAA usually sufficing*. There are almost no hourly data available for periods of this length, and in

any case designers are unlikely to run a loads program for more than one year because of the high costs. The solution is to find a typical

Averages of long-term climate as used here may be represented by

'normals', averages for a special period of record. Current normals

from NOAA are for the period 1941-1970. Other sources may use different periods, which may result in different values for normals in the same location. Longer periods generally show closer correspondence

between their normals [2].
year or representative year that represents as closely as possible the

long-term record in energy terms. There has been considerable interest

in this subject, and several approaches have been developed or are under development [3, 4, 5]. Although this project was not originally

intended to create representative year weather tapes, the method developed to extrapolate data geographically has potential for generating

representative hourly data for a long-term period of record.

Two techniques for creating synthetic hourly weather data are proposed

and tested. The first technique is a process to select intervals of

real weather from a multi-year hourly weather tape or tapes to link

together to create a composite synthetic year whose monthly averages

represent monthly averages taken from the long-term record (hereafter

referred to as "composite years"). The long-term month averages can be

taken from: (1) the same station as the hourly data tape, in which case the selection technique creates a year representative of that station; or (2) a second-order station geographically removed from the hourly

data station, in which case the selection technique creates a year

representative of the removed station made up of segments of actual

weather recorded at the hourly data station. The extrapolated year

is "representative" because its monthly averages approximate the new location's averages as closely as possible.

Clearly there are climatological and geographical limits to such extrapolation. These are discussed in the report.

The second technique for creating synthetic hourly data is a process to

mathematically adjust a first-order stations 's hourly data values in an hour-by-hour manner. The technique basically has two purposes: (1) The

first is to make the monthly averages of the adjusted tape match the

long-term averages for the second-order station almost exactly. This

purpose is the same as the selection procedure described above but,

when applied in sequence to the selection procedure, allows additional

refinement. (2) The second purpose is to adjust the data for a real

period of time at the hourly station to create a synthetic hourly tape

for the identical period at a second-order station or at an experimental

site. This may be useful in energy audits and experiments in which

observed fuel use in test buildings is being compared to fuel use computed from climate data, or to the climate data itself. The factors

used to adjust the data could be taken from the ratio of long-term

averages for first- and second-order stations, or from comparison of

averages for the actual period of interest as recorded at a second-order

station or at an experimental site.

Again, the limits to which such hour-by-hour adjustment can be used to

extrapolate climate data geographically are discussed in the report.

The two techniques are embodied in two appended user-interactive FORTRAN

programs, SELECT and ADJUST. The programs have been made interactive in

order to allow the user to see significant parameters of the source data

tape while considering the input parameters, and to summarize these for

the selected and/or adjusted output tape. The programs read in and

3
write out data from files of previously decoded data. The programs may

be easily adapted to the various formats for decoded weather files used

by the major building energy analysis programs.

Facing page:

Weather data is available in two

primary forms; published summaries

containing averaged information

and magnetic tapes containing

hourly observations. Energy

loads programs, such as DOE-2

and NBSLD require hourly weather

data input.

4
PROGRAM MANUAL

SERIES 69

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2 . METHOD

2.1 SOURCES OF MONTHLY SUMMARIZED DATA

S3mthetic weather data is usually created on a monthly basis, because

summarized climate data has been traditionally presented in terms of

monthly averages, and because a monthly profile of building loads gives

a sufficiently fine definition of a building's annual energy performance.

Weather data are available from NOAA and the Armed Forces in various

sources described in the Selective Guide to Climatic Data Sources [6]

5
and the Guide to Standard Weather Summaries and Climatic Services [7].

The chief sources are NOAA's Climatography of the States, Local Climatological Data [8]; NOAA's Climatic Atlas of the U.S. [9]; and the Armed

Forces' Manual 88/29, Engineering Weather Data [10]. The other major

national source is the ASHRAE Handbook of Fundamentals [11]. Detailed

local data may frequently be obtained from existing sources such as air

pollution control districts, utilities, industry, universities, and

experimental institutions. In addition, daily or limited hourly records

may be gathered at experimental buildings during specific periods while

energy consumption is being measured.

The U.S. has approximately 275 first-order NOAA stations and 5,000-6,000

substations publishing monthly means. There are approximately 175 U.S.

military bases collecting the equivalent of first-order station data.

The number of other possible sources is not known.

2 . 2 TYPES OF SYNTHETIC WEATHER DATA

A variety of techniques can be used to create synthetic weather data.

These techniques differ in the extent to which the sjmthesized data is

artificial. A totally synthetic technique is the hourly weather generator developed by Degelman [12], which uses as input only mean values

from statistical summaries for a site or period of record. The hourly

values are generated by a combination of deterministic modeling of daily

and yearly cycles (temperature and insolation) and probabilistic selection of weather variables from empirically-based distributions of the

variables. The coincidences and sequences of hourly data thus created

are totally artificial, although based on patterns analysed from real

weather. The technique has the advantage of not requiring any hourly

data as input.

In contrast, the selection technique used in SELECT compiles a composite

of actual weather data segments into a composite synthetic year representative of a site or period of record. It requires as input both mean

values and a multiyear record of actual hourly weather observations on

tape. Although the ordering of the data points in a composite year does

not correspond to any real year, the data coincidence and sequences

actually did occur* The 'Typical Meteorological Year' (TMY) [4] and

'Test Reference Year' (TRY) [3] representative weather data tapes are also composite years, created by different selection techniques. Selection techniques such as these cannot create a composite year whose

monthly means exactly match those of the input summarized data. This

is because the period of record of available computerized weather data

is limited (less than thirty years for most stations) and the number

of segments available for selection is restricted.

A third technique that is intermediate between the totally artificial

approach and the selection approach is used in ADJUST. It modifies the

actual weather observations from a real or representative year on an hour-by-hour basis, to make a synthetic year whose means exactly match

those of the input summarized data for a site or period of record.

6
2.3 SEGMENT SELECTION TECHNIQUE

2.3.1 Reasons for Selecting Unmodified Data

There are several advantages to the composite approach of using selected real weather data segments to represent a location's climate. These

advantages apply particularly when creating climate data representative

of the long-term summarized record for the same site as the source

input tape, but they may also hold when the selected year is being

made for a location remote from that of the source input tape.

First, in using actual unmodified data, the user is guaranteed that the

interaction between climate variables in the composite year actually

could happen, because it did. Inexpert modifying or synthesizing of

hourly data could result in unlikely or impossible combinations of

climate variables that could affect computed energy use.

Second, the unmodified data by default incorporates certain local subtleties of weather that may be important in determining the suitability of

a building's design to its location, but that would not be characterized in available published summaries, and would be very difficult to

analyse and incorporate in a totally synthetic hourly-data-generating

technique. On a daily basis, such subtleties include consistent morning

fogs, sea breezes, valley winds, and afternoon cumulus cloud development.

On a seasonal basis, the correlation of high winds with low temperatures

following cold front passages increases the effect of low temperature on

building energy consumption. In order to fully characterize a particular

climate, the synthetic climate must be able to capture the diurnal

temperature range and distribution, the distribution of cloud cover and

wind over the day, and the correlations of wind, hximidity, cloud cover,

and temperature distinctive to the location. The use of unmodified

data tends to retain such subtle characteristics. The hour-by-hour

adjustment procedure that modifies actual climate data may also keep

the subtleties of the original unmodified data, providing the adjustments are not excessive.

If the unmodified composite data are being used to represent the

climate of the same location at which they were recorded, the selection

technique's ability to retain daily and seasonal subtleties given an appropriately-sized sample is highly advantageous. The user should note

that if the composite data are being used to represent a different

location, the retention of such subtleties may be less important, or counterproductive. Care must be taken to assure that the climate characteristics are similar across the geographical distance. Chapter 4

discusses such concerns, and suggests that the user consult a meterologist when uncertain.

Third, the selection technique described here (as also the totally-artificial-weather generating technique) can create a typical year from a multiyear period of record, whereas an hour-by-hour adjustment procedure

necessarily requires a representative base year to adjust from. Without

a representative base, for example if one were to use one year selected

7
at random, the adjustments could well be excessive and the adjusted

values have Incorrect distributions. A procedure will be proposed below

to use the selection and adjustment techniques in sequence, with the

selection procedure being used to create a representative base year

requiring minimal subsequent adjustment.

2.3.2 Selection Algorithm

The weather data for the composite years are chosen by a segment selection algorithm that considers the effects of the maior climate variables

on building energy consumption. The core of the algorithm is a linear

model predicting building energy consumption as a function of daily (or

monthly) averages of temperature, sun, wind, and humidity. The coefficients of this model are used to weight the deviations between the segment averages and the long-term monthly input averages for these climate

variables. The weighted deviations are then summed to form a score. Of

all the segments being evaluated, the segment with the lowest score is

selected as the best representative of the month.

The energy-climate model is intended to represent a fairly wide range of

buildings in a wide range of climates. It was developed by the authors

for an hourly climate data abbreviation technique [13]. It is based on

statistical analysis of the heating and cooling requirements of three

different building types in response to climatic variables in three climates. The buildings used in the analysis are similar in that all are moderately well-insulated small buildings sensitive to climate through

their envelopes ('envelope-dominated'), in order to get fairly high

responsiveness to climatic influences. The influences of climate on the

less sensitive, large 'system-dominated' buildings were not addressed in

the analysis leading to the model. It may be that synthetic weather

data developed for sensitive buildings would be appropriate for less

sensitive buildings. This was investigated only within the context of

envelope-dominated buildings, where sensitive and insensitive buildings

were shown to respond to climate variables similarly, allowing one weather data set to be used for both.

The three buildings used in the analysis are a lightweight conventional

frame ranch house of 112 m^ (1,200 ft^), a more massive passive solar

variant of this ranch with additional south glazing, and a very massive

masonry two-story interior townhouse unit of 120 m (1,280 ft^). The

buildings were designed to meet ASHRAE 90-75 insulation requirements

for a site with 5,000 heating degree days (base 65°F), but because the

buildings used such conventional insulation components as R-11 wall

batts or R-10 foam panels, they actually met the ASHRAE standard across

a wide geographic zone up to 7,800 heating degree days [14]. The buildings were simulated on NBSLD under standardized operating conditions.

[15, 16] The climates used were Minneapolis, MN, Washington, DC, and

Phoeniz, AZ, with TRY tapes [3] used as data for the simulations and

for the regressions of climate against energy.

The coefficients for the model were determined by a regression analysis

of daily climatic averages against daily energy results. The relative

8
influence that climate variables had on heating and cooling requirements

varied somewhat from building to building and climate to climate. However, the range of variation of the individual coefficients for heating

and cooling modes, in three buildings in three climates, was sufficiently small that a single averaged set of coefficients could be employed

in the model.

The model was tested on a one-story office building in three climates

additional to the ones above: Miami, FL, Colximbia, MO, and Boston, MA.

The model worked successfully in selecting segments for piecing into

abbreviated weather years for both this building and the three previous

buildings, even though their thermal responsiveness varied greatly.

The model for the segment-selecting score follows:

Score = 14.3|ADBI + 4.9|ACC| + 0.37|aWSDB| + 9.66x10^ | aMR | (1)

The segment with the lowest score of those evaluated is selected as being most representative of the input climate in energy terms. The score is an estimate of the difference in loads between the segment

being evaluated and a segment with the input averages, in MJ/day, and

ADB = deviation of average dry bulb temperature (°C)

between segment and monthly input. The 'average

temperature' is computed either as a mean of all

hourly values or of daily maxima and minima (as

used by the Weather Bureau) , depending on the

nature of the input data being selected toward.

ACC = deviation of average total cloud cover (tenths

of cover) between segment and monthly input.

AWSDB = deviation of average windspeed-times-(18 .

3

minus dry bulb temperature) between segment and

monthly input. The temperature 18. 3 "C is the

familiar degree day base 65"?, used as an approximate base for both heating and cooling.

AMR = deviation of moisture ratio for (kg per kg

dry air) between segment and month.

The relative influence of each climate variable on heating and cooling

depends on the magnitudes of these coefficients and on the range that one may expect of the variable. Assuming a range across a month of 22 "C

(40**F) dry bulb temperature, 0-10 tenths cloud cover, 0-13 ms~^ (0-30

miles hr"'-) wind (at -4°C or 25°F),*and 0.013 kg per kg dry air

(maximum moisture range, -4 to 18 "C or 25 to 65 °F); the relative importance of ( temperature: cloud cover:wind:humidity) in this equation is

(2.5:1:1:1). In warmer conditions, the influence of wind diminishes

and that of humidity increases.

9
A final consideration in selecting representative segments is the

observed fact that during temperate months, heating and cooling

requirements are a function of excursions from the mean temperature

rather than the mean temperature itself as considered by the model for

scoring segments. The temperature distribution for each month should be

represented in the composite month. This cannot be handled by the scoring model itself, and is therefore done in the selection algorithm by a procedure organizing the temperature distribution of the selected segments to assure a typical monthly temperature distribution and range.

The way in which means and distributions are handled in the algorithm

is discussed below.

2.3.3 Implementation of the Algorithm

A FORTRAN program SELECT was written to process several years of hourly

weather records for selecting single synthetic composite years. The

version listed in Appendix A processes a minimum of 3 and maximum of 10

years from up to 10 source locations. The number of source years could

be increased with increased storage.

» The synthetic composite months are made up of three linked segments of

9, 10, or 11 days, depending on the length of the month. The length of

the segments were determined by several considerations. First, the segments had to be long enough to contain the full length of naturally

occurring climatic cycles, so that the segments will contain sufficient

ranges of each climatic element. Since the primary cycles within a month are daily cycles and 4 to 7 day weather cycles, segments over a week in length will tend to include these automatically. Second, the

segments need to be long enough to minimize the effect of discontinuities at the junction between the segments. Segments pulled out of their

normal sequence, and linked together with up to three weeks of data

missing in between, may contain a larger climatic change between the

last hour of the preceding segment and the first hour of the subsequent

segment than would a naturally embedded sequence of hourly climate data.

The energy implications of this step change depend on the natural variability of the climate and the mass of the building. Inspection of

hourly loads for natural and composite climate sequences show the energy

differences resulting from the step change to be insignificant for segments a few days in length. Since the segments are substantially longer

than this, the authors have not felt it was necessary to smooth the

junction between segments. Third, the segments need to be short enough

so that the source population contains a large number of segments to

select from. The number of segments of length n available from a given

source month of length m equals (m - n + 1), overlapping being permitted. The larger n becomes, the smaller the number of segments available. Fourth, the segments must be short enough to provide variability

in the averages of their climatic elements. The longer the segments,

the more restricted the range of their averages around the averages of

the source month itself. If the input averages are different from those

of its source, it becomes harder to find segments with averages matching

the input averages.

10
A series of runs were made with segments of differing length to help

decide on the most effective length. In these runs, segments were

chosen with averages matching the appropriate monthly averages as closely as possible. With 10-day and shorter segments, the restricted

range of temperature in the composite month was the limiting factor in

energy prediction, causing underprediction of energy. Longer intervals

would alleviate this only slightly, and the increased difficulty of

finding longer segments that matched the input temperature averages

offset the advantage. It became clear that the preferred segment length

would be between 7 and 10 days (3 to 4 segments per month). The ten day

length was chosen in order to assure inclusion of climatic patterns.

After this choice, measures were examined to increase temperature range

and to increase the population of source segments.

The population of source segments was increased to include the two

months adjacent to the month being selected. This was done because the

nature of the climate is unlikely to change from one month to the next,

and the small change in monthly averages from month to month may be

helpful in making more suitable segments available for selection when

the input averages differ from the averages in the source data population. There is, thus, a source data population for each month of about

83 ten-day intervals per year of hourly weather record. For the average

TD 1440 ten-year record, the 830 segments were found to provide very

satisfactory fit-capability in practice.

The temperature range problem was then analyzed to determine to what

extent the monthly range and distribution are caused by climate through

the month, as opposed to the variation induced by weather cycle fluctuations (on the order of 4 to 7 days). The annual march of temperature

was very significant in transitional months but lost importance in the

peak hot and cold months (generally January and July).

Since segments appreciably shorter than 30 days were not suitable for

representing the range due to annual march of temperature, some other

means of representing this range in the composite year had to be found.

By selecting a month's segments to a distribution of input averages

around the monthly average, the range and distribution of hourly temperatures in the month can be approximated. Considerable effort was expended to find an available published measure that could be adopted

to this purpose. The most common statistic, the standard deviation of

month temperature means around the long-term (30 year) mean for that

month, could not be used effectively. It became clear that the measure

would have to be derived by processing the input weather tape.

The SELECT program calculates what we have termed the "average segment

temperature march for the month." It is found by summing the absolute

values of the deviation between each segment's average temperature and

the monthly average from the input tape, then dividing by the sum of

the absolute values of the difference in days between midmonth and the

middle day of each segment. This is a measure of the average march

of temperature from one segment to the next through the month.

11
The population of segments for computing the average temperature march

extends half a segment length beyond the ends of the month; e.g. for

the 10 day intervals, the first interval begins five days before the

beginning of the month and the last segment ends five days after the

last days of the month. This is done to equalize the weights of days

in the month because when counting the overlapping population of segments, the central days are counted more often than the end days. By

extending the source month, the first day of the month is counted six

times, the second seven, and so on until the fifth day is counted ten

times; all the central days until five from the end are counted ten

times, then decreasing as at the beginning of the month. The influence of the periods extended beyond the ends of the month is relatively

small, due to the low number of times each day is counted, and compensates for the reduced weight of the first and last five days of the

month itself.

SELECT incorporates the march of temperature value in the selection

procedure as follows. Three segments of 9, 10, or 11 days are combined

to make a month of the appropriate length, with the longer segment in

the middle. The middle segment is selected first, using the selection

model (Eq. 1) with the monthly mean values for each climate element as input.

The second segment selected is picked on dry bulb temperature alone,

and is selected to have a mean temperature displaced from the mean

monthly temperature either up or down by ten times the "average segment

temperature march for the month". This value is the median value for

segments in the lower or higher third of the month. The choice of

whether the higher or lower part of the month is picked first is somewhat arbitrary - it is based, for each month, on which segment would

be more difficult to obtain, given the input average for the month

and the average for that month's population of hourly data. E.g., if

the input average is lower than that of the source data, the second

segment selected is displaced downward from the mean. The user of

the program will have some control over the extent of displacement;

this is discussed later in 2.3.4 under use of the program.

The second selection will have produced a perturbation in the composite

month's temperature selected so far, and the other climatic elements

will have varied as well. The third selection attempts to balance out

the perturbation. The climatic elements of the two selected segments

are combined to determine the requirements for each climate element

in the third segment to provide the combination of the three segments

with climatic averages close to the input averages, as weighted by the

selection model. As a result, the third-selected segment will contain

a temperature deviating from the mean in the opposite direction as the

second-selected segment.

Because it was found that the third selection often could not bring the

combined averages to the mean using the source segments available, a fourth selection is made to re-select the second-selected segment based

on all climatic elements. The requirements for selection are the

12
climatic elements needed to bring the combined averages to the means.

In this selection, possible unusual second-selected segments (selerfed

on temperature alone) tend to be eliminated. The process is repeated

one more time, reselecting the third-selected segment based on the

combined average of the other two. In practice, this fifth selection

frequently selects the same segment as the third selection, so the

refinement is not carried further. Note that the month's central segment has not been reselected, and is the most "average" segment in the source population in the terms of the selection model.

The three selected segments for each month are then arranged in sequence

in order of the temperature march through the year; rising temperature

in spring and falling in autumn. The sequence during the hot and cold

"peaks" is: the average temperature in the middle, the segment with

the higher dry bulb average placed next to the adjacent month with

the higher dry bulb average, and the segment with the lower dry bulb

average next to the adjacent month with the lower dry bulb average.

In order to avoid repeating the same climate data, the program will not

select segments from the source tape if their span overlaps previously

selected segments by more than five days.

The program provides an optional extra one-week initialization period

needed for some transfer function loads programs. It is comprised of a duplicate of the first week of the input file.

2.3.4 Use of the Program SELECT

The user assigns between three and ten years of source weather tapes

(of data files) in NBSLD-decoded format [16]. The decoded tapes contain

the climate elements in groups of 24, followed by date and city code.

The source tapes may come from one location or from many; it is possible

that a user may wish to extrapolate geographically from first-order

sources bracketing the location for which a composite year is being

generated. The source tapes are combined, regardless of origin, into

a pool from which the segments are selected.

The program processes the data and prints the monthly averages of the

climate variables in the pool for each month: dry bulb temperature,

daily range of dry bulb temperature, standard deviation of daily average

temperature around the monthly mean, heating and cooling degree days,

dewpoint and wet bulb temperatures, daytime and 24-hour average cloud

cover, windspeed, and barometric pressure. The dry bulb temperature is

computed either as a mean of all hourly values, or in the form of NOAA

published records: the average of the daily maximum and minimum. The

user should choose the form consistent with the input data that are being selected toward. The degree days are calculated to base temperatures input by the user.

The user enters below these values the input averages desired for the

composite year. The input averages required are: dry bulb temperature

(from many possible sources), dewpoint temperature or wet bulb

13
temperature (from the Climatic Atlas or Air Force Manual AF 88-29),

daytime or 24-hour cloud cover (from local weather summaries or the

Climatic Atlas), wind speed (from the same sources), and barometric

pressure averages or the elevation of the station being selected for.

If information for a given element is unavailable, the user may choose

to default and echo average values from the input tape. A monthly

adjustment factor is also called for, to expand or contract the distribution of temperature in each composite month.

In the absence of information on monthly temperature distribution, the

user inputs 1.0, giving approximately the same temperature distribution

as that of the source data pool. Subsequent iterations may be desired

if the composite month's daily standard deviations differ from known

desired values. The exact influence on daily standard deviation of the

adjustment factor, which actually adjusts segment temperature march,

cannot be determined in advance.

The input averages are converted by the program to variables used in the

selection algorithm. Two transformations are made: first, the dewpoint

or wet bulb temperature and the barometric pressure or elevation are converted to a moisture ratio using the ASHRAE-approved psychrometric

subroutine [16] and the NACA standard atmosphere [17]. Second, the

windspeed and the dry bulb temperature difference from 18.3**C (65°F) are multiplied to fora one windspeed-drybulb product as used in the

algorithm.

The segments are then selected. The program prints out a synopsis of

the segment selection sequence, the source location, date, and year of

the segments chosen, with a comparison of the composite and input monthly

climatic averages of each climate element. The daily standard deviation

of the synthetic month is printed for comparison to the source data

standard deviations. The segment ranks given reflect the estimated

energy performance of each segment in MJ/day. An example of an input

stream and output is presented in Appendix D.

The user may choose to either use the resulting composite year as is, or subsequently adjust the hourly values to make the monthly averages of

the composite year match the input averages exactly, in what then becomes

a totally synthetic weather year. The adjustment technique is described

in the next section. Chapter 4 discusses considerations in using the

two programs in combination.

2.4 HOUR-BY-HOUR ADJUSTMENT TECHNIQUE

2.4.1 Reasons for Hour-by-Hour Adjustment

Given a single year, it is possible to adjust each hourly value in the

year to cause the monthly averages of each climate element to equal

given input averages while maintaining relationships between elements.

An approach like this has two advantages over the composite year

approach described above. First, the monthly averages are duplicated

exactly, not approached to the best of the source data pool's ability.

14
Second, any composite data technique using multiday segments will not

be able to select for climatic characteristics that occur within the

span of a day. The one diurnal characteristic commonly recorded is the

daily range of dry bulb temperature, and monthly averages of daily

range are widely available. This statistic is very significant in

predicting energy use in transitional months where the daily average

temperature is near the balance points of the building and heating

or cooling loads do not exist all day. A composite month with too

small a daily range will underpredict heating and cooling loads during

such months, and one with too great a daily range will overpredict.

The composite weather data accordingly should not be used in extrapolations where diurnal climate characteristics differ markedly between

stations. By adjusting weather data this restriction can be relaxed

for whatever climatic elements diurnal characteristics are known. There

is a risk attached to this, in that the data are now truly synthetic

(never actually occurred), and the combinations of climate elements

in the synthetic year may begin to deviate from physical reality. The

extent to which such deviations will affect building energy prediction

must be determined empirically.

2.4.2 Adjustment Procedure

The adjustment of hourly data is done differently for the various

climate elements. The following techniques are employed in the FORTRAN

program ADJUST:

** Dry bulb temperature

Three adjustments are made; for the monthly average, the

distribution of daily averages in the month, and for the

distribution of hourly values in the day. As in SELECT,

the user chooses the form of 'average temperature' consistent with the input data being used: the average temperature may be either the mean of all hourly values or the

mean of daily maximum and minimum, as used in NOAA

published records.

1. Monthly average: for each month, the difference between

source and desired input average is found. This difference between monthly values is added to each hour of the

month.

2. Monthly standard deviation of daily averages: the ratio

of the desired standard deviation over that of the source year is determined on a monthly basis. For each day, the

deviation of the daily average from the monthly average of

the source year is found and multiplied by (the ratio minus

1). This value is added to each hourly value in that day.

This does not modify the diurnal distribution of hours, but

expands or contracts the distribution of daily averages.

15
3. Daily range: the ratio of the source year and desired

input value of daily range is determined for each month.

The hourly adjustment is carried out by multiplying by

the difference bewteen each hour's temperature and the

average for that day. The adjustment expands or con- tracts each day's distribution of hourly temperatures.

Dewpoint/wet bulb temperature

The ratio of the dewpoint depressions (dry bulb temperature - dewpoint temperature, input average over, source month average)

is multiplied by the hourly dewpoint depression for each

hour of the real month, and subtracted from the adjusted

hourly dry bulb temperature, to give the adjusted hourly

dewpoint temperature:

DPTah = DBTah - ""pg ^"^'""^ X DDh source M source where DPT is dewpoint temperature

DBT is dry bulb temperature

DD is dewpoint depression

A represents "adjusted"

M represents monthly average

and H represents a given hour

The same process is used to determine hourly adjusted wet

bulb temperatures. The ratio of the source and input average monthly wet bulb depressions is multiplied by each

.hour's wet bulb depression and subtracted from that hour's

adjusted dry bulb temperature.

The monthly average wet bulb and dew point temperatures are determined as an average of all hours in the month, since

this is the basis for the published values. If wet bulb

temperature and dewpoint temperature cannot both be found,

the user may enter either and the other will be calculated

by the ASHRAE psychrometric subroutine on an hourly basis.

It should be noted that the adjustment procedure partially

shifts the distributions of dewpoint and wet bulb temperatures from their original distribution to follow the hourly

adjustments made to the dry bulb temperature.

Wind

Each source year hourly velocity value is multiplied by

the ratio of the monthly source average to the monthly input

average. Because the distribution is bounded on one end

by zero velocity, this adjustment increases variability of

hourly velocities in windier locations by the same ratio.

Although velocity variability does tend to follow mean

16
velocity in general, the relationship between variability

of hourly values and monthly averages is complex, depending

on a variety of climatological factors. Different locations

experience different weather systems and terrain, causing

their distributi ons of hourly velocities to be different

even when their monthly averages are the same. In addition,

different locations have different mixes of wind directions

that are included in but not specified in the monthly wind

velocity averages. Each direction reflects a different

weather system or terrain influence. Thus differences in

the distribution of wind directions will also affect the

relationship between monthly velocity average and distribution of hourly velocities in the month.

Readily available climatological information will not provide information to define the wind velocity variability at

the input station. In view of this, and of the great simplifications made in prediction of infiltration and natural

ventilation in state-of-the-art load programs, a model more complex than the linear approximation adopted here is not

felt to be justified.

Cloud Cover

Cloud cover adjustments are made to total cloud, as used by

current loads programs to compute the solar radiation

incident on a building. The Kiraura and Stephenson [18]

algorithm that has recently been integrated into NBSLD,

replacing the original ASHRAE algorithm, computes radiation as a function of total radiation and cloud type, with

cloud type divided into two categories: cirrus (including

cirrostratus) and non-cirrus. ADJUST determines cloud type

from the weather tape variable "type of cloud in the lowest

layer." The DOE-2 radiation algorithm [19] uses total

cloud and type of cloud in the lowest layer as well, but

with three categories of clouds: cirrus, stratus, and

other. ADJUST will work for either algorithm, and spreads

the adjustments described below proportionately among the

three cloud types.

The adjustment of cloud cover is complicated by the fact

that the cloud cover classes fall between two bounds, 0 and

10, in whole numbers. In addition, examination of the distributions of cloud cover hourly observations in various

locations around the climatic regions of the U.S. shows a bimodal distribution with between one-half and two-thirds

of the classes recorded are either 0 or 10, at the bounds

of the distribution. Figure 1 and Table 1 show some example

distributions. Similar but somewhat less extreme distributions are observed in Great Britain [20]. This means that

the application of a ratio to the data would alter the distribution significantly and perhaps cause the elimination

17
CLOUD DISTIBUTION BY TENTH AND YEARLY AVERAGES.

ALBANY JERSEY LAKEHURST NEM PHILADELPHIA PHOENI

CITY YORK

TENTH HOURS HOURS HOURS HOURS HOURS HOURS

0 1473 2179 1697 2092 1683 4969

1 237 333 267 320 349 279

2 354 363 447 415 360 274

3 340 404 426 344 436 255

4 352 366 40 5 264 323 177

5 335 161 306 292 274 146

6 366 287 302 313 31 2 240

7 467 393 478 318 374 269

8 625 523 637 492 499 376

9 429 434 521 491 535 392

10 3762 3295 3072 3399 3403 1446

AVG. 6.52 5.72 5.85 5.85 6.09 3.06

Table 1. Example Distributions of Cloud Cover by Tenths.

DISTRIBUTION OF HOURLV CLOUD UALUES FOR 1951

4eee

TENTH

SOLID-ALBANY AUG. 6.53 DOT-LAKEHURST AUG. 5.85

DASH-NEU VORK AUG. 5.85

Figure 1. Example Distributions of Cloud Cover, by Tenths.

18
of the 10 tenths cloud cover class. Additive corrections

could cause possible elimination of either the ten or the zero cloud cover classes.

To solve this, unit adjustments are made to that number

of hourly cloud observations necessary to obtain the input

average in the adjusted data. The hours to be adjusted are chosen in a way that disperses them throughout the month.

For the 10 and 0 cloud classes, priority is given to hours

that are not embedded in strings of consecutive hours with

the same cloud cover class, in order to break up storms and

clear spells as little as possible.

The number of unit adjustments to hourly cloud values is

determined as follows: since the monthly averages are the sum of hourly values in the month divided by total hours in

the month, the number of unit adjustments is the desired

change in source-to-input cloud averages times the number

of hours in the month. The change in source-to-input cloud

averages is the difference between the 24-hour monthly cloud

averages for source tape and input data. However, daytime

averages are often the only data available in published

form. If daytime cloud averages are being input, the change

in 24-hour monthly cloud averages is approximated as follows.

A subroutine calculates daytime length and the time of sun- rise from latitude, longitude, and time zone of the remote

station as input by the user. These are used to find the

daytime monthly cloud averages for the source year. The

difference between the input daytime monthly cloud average

and the source-year daytime monthly cloud average is found,

and multiplied by the ratio of the source 24-hour monthly

cloud average over the source daytime monthly cloud average.

This approximation accounts for possible differences in daytime and 24-hour cloud, and allows the number of unit

adjustments to be determined.

The unit adjustments are applied to an equal number of hours

from each class of cloud cover tenths. For example, in

adjusting the 24-hour monthly cloud average for a 30-day

(720 hour) month downward 0.1 tenths, it is necessary to

adjust a total of 72 hourly cloud observations down one

tenth. Since there are 10 classes available (10 through 1 tenth) for downward adjustment, one unit is subtracted from

7 hourly cloud values for each class. This alters the distribution of values by class only in the 10 and 0 classes.

It was felt that the small number of values observed in the

intermediate (1 through 9) classes did not justify the additional alteration of the distribution of values within them.

This decision seems to be borne out by inspection of hourly

distributions of adjacent stations, where changes in the means produce no consistent trends in the intermediate

classes. Figure 1 shows that when moving from Lakehurst

19
or New York, with essentially the same cloud cover mean,

to Albany, with a higher mean, the number of hours in the

intermediate classes for New York need to be increased while

for Lakehurst they need to be decreased.

° Barometric Pressure

The difference in source and input barometric pressure is

found from the source tape monthly average and the monthly

input average given by the user. This difference is subtracted from each source tape hourly pressure in the month.

If monthly averages are not available, elevation differences may be input by the user. These are converted to

pressure on an hour-by-hour basis using the coincident dry

bulb temperature and source pressure.

2.4.3 Use of the Program ADJUST

The user assigns a source weather tape in NBSLD-decoded format. The

composite years created by the SELECT program are in this format. The

program processes one year's worth of data and prints out averages of

climate elements for each month: dry bulb temperature, the cloud cover statistic being used (24-hour or daytime), wind speed, dewpoint temperature, wet bulb temperature, barometric pressure, standard deviation of

daily averages around the monthly mean, and daily temperature range.

The user enters below these values the input averages desired for the

adjusted year. Options are given for: 24-hour or daytime cloud cover,

dewpoint or wet bulb temperature, and barometric pressure or station

elevation. If input values are not available for standard deviations

of daily temperatures around the monthly mean, the user should echo the

values given for the source tape.

The program will skip any desired number of days on the input tape

before adjusting a full consecutive year. The user should remember to

skip the seven-day initialization period provided by the SELECT program,

if that option had been exercised in creating the composite year. The

program will begin on any desired month of the year on the input tape,

providing that a full year of data follows that month.

An example input stream is given in Appendix E.

Facing page:

The ADJUST program^ used in an interactive mode, describes each

required input and cues the user upon completion of the program

run,

20
3. TESTS AND RESULTS

3.1 TEST PROGRAM

The SELECT and ADJUST techniques were tested across a network of cities

in the Northeast: New York NY (LaGuardia Airport), Jersey City NY,

Lakehurst NJ, Philadelphia PA, and Albany NY. New York and Jersey City

are considered central cities in this network, acting as data sources

for making composite and synthetic tapes for all the cities. Two tenyear TD 1440 tapes were obtained for New York and Jersey City, each with

the period 1949-59. The two nearby stations experience different daily

temperature range and wind speed, and allow comparison of how differences in the source data affect the weather data synthesized for remote

stations

.

21
The year 1951 was a fairly typical year in New York City; this is the

year selected by the "Test Reference Year" procedure [3] from the 27-

year period 1948-75. (The TRY procedure picks a complete year from a record that contains the smallest number of months with large deviation

from their long-term temperature averages. It is meant to produce

fairly representative single data years for comparative testing of

building energy systems). 1951 tapes were obtained for Albany,

Lakehurst, and Philadelphia. In this test program, the monthly aver- ages for 1951 were substituted for the long-term monthly averages that

would normally be input to produce representative composite or synthetic years, not because they are as good as long-term averages, but

because they allow much easier validation without impairing the- test's

precision. If long-term averages had been used to create actual representative years, the validation would have required 30-year energy loads

runs to determine long-term energy performance. The costs of such an approach outweigh the advantages.

The tests were designed to test the extrapolation potential of the

SELECT and ADJUST programs from New York and Jersey City to the other

stations. Composite and synthetic years were created from the New York

and Jersey City 10-year source data tapes for all cities in the network

using the 1951 monthly averages of climate elements in those cities.

The 1951 averages for each city were obtained by computer processing

that city's 1951 weather tape. Each city's composite or synthetic 1951

year could then be directly compared to its real 1951 year, both climatically and in terms of its influence on the energy performance of

buildings calculated by NBSLD.

The tests were carried out in several ways for different purposes:

(1) The composite years created by SELECT for 1951 New York

and 1951 Jersey City from their 1949-58 hourly records and

1951 climatic averages were compared to the real 1951

years for those stations. This tests the ability of

SELECT to produce representative years. The input averages normally would be long-term values rather than 1951

values.

(2) Composite 1951 years were created using SELECT for the

remote stations Lakehurst, Philadelphia, and Albany. This

tests the ability of the selection technique to extrapolate

across geographical distances. These distances are generally greater than what the selection technique was intended

for, but show the capabilities and limitations of the

technique

.

(3) The adjustment technique was used to create synthetic 1951

years at remote stations using the real New York and Jersey

City 1951 tapes. This tests the ability of the adjustment

technique to extrapolate hourly weather data for a real

period of time to a location where only climatic averages

are being recorded. Data created this way would be used

22
in analysis of data collected in experimental buildings on sites with limited climate measuring capability.

The performance of this "real period" synthetic year might be expected

to be better than a synthetic year created using a composite 1951 at the source station. This is obvious in that a composite 1951 at the source station cannot have day-to-day similarity to the weather patterns of the

real 1951 at the remote station. Such similarity in weather patterns occur throughout any real period of time in two reasonably proximate

locations

.

(4) There are two ways the selection and adjustment techniques

can be used together to create representative synthetic

weather data at a remote location. First, the selection

technique could be used with the Input averages of the

remote station, to create a composite year at the remote

station that would then be adjusted so that its averages

matched the input averages exactly. The selection step of

this is tested in (2) above.

Second, the selection technique could be used to create a representative year at the source location Itself by using

long-term input averages for the source location (tested in

(1) above). This composite year would then be adjusted

using the long-term averages for the remote station as Inputs to the adjustment technique.

Synthetic 1951s were created by both methods for the remote stations

to compare results. It should be noted that the adjustment technique

could be used to Improve the performance of representative years at

their source location. However, because the error resulting from using

SELECT alone for this purpose is small, it was felt that ADJUST would

be better tested by creating years for remote locations.

All tests of loads prediction were done by using the Fort Meyer Building,

for which the NBSLD input stream has been published [16]. The building

is a zone of a one-story office building of 303.3 m^ (3265 ft^), with

330 mm (13 in.) uninsulated brick walls, a 1.94 m^ °C W"-"- (R-11) celling

below an attic, 14.4 m^ (155 ft^) of glass facing west, and 7.4 m^

(80 ft^) facing east. The north wall is a partition to another zone of

the building. The assumed interior conditions include thermostat settings of 20.0°C (68°F) for heating and 25.5°C (78°F) for cooling, with

nighttime setbacks to 15.5°C (60°F) and 29.4°C (85°F), respectively.

This building is substantially different from the three buildings used

to develop the selection model (Eq. 1, described in 2.3.2.). The building is more massive and more dominated by interior loads than the three

houses. It remains, however, sensitive to climate through the building

envelope, as reflected in plots of daily heating and cooling requirements

for various seasons of the year (Figure 2).

23
3.2 COMPARISON OF WEATHER DATA BETWEEN STATIONS

Tables 2 through 5 and Figures 3 through 5 give the comparative monthly

averages, processed by computer from the real 1951 hourly weather

records, of the climate variables in the different stations of the network (New York, Albany, Lakehurst, Philadelphia). The corresponding

values for the composite years produced by SELECT are also given on the

tables. Consistent displacements are observed between stations in the

variables temperature, dewpoint, windspeed, and barometric pressure.

Table 6 gives the means and ranges of climatic elements in New York over

the lO-year record. The ranges also appear in Figures 3 through 5. The

corresponding values for Jersey City can be found in the example SELECT

run given in Appendix D. These ranges are an indication of the limits

of select's ability to find segments representative of remote locations.

The distribution of climate elements within the day has been compared

only for dry bulb temperature, using the variable daily range. Tables 2 through 5 and the daily range graph in Figure 5 present average daily

ranges, by month, for the four stations. The relatively low daily range seen in New York/LaGuardia was seen to have a significant effect on select's ability to extrapolate representative energy-predicting years

from New York to remote stations. This resulted in the inclusion of

Jersey City in the network, and is discussed in section 3.3.

Figures 6 through 8 present simultaneous daily climate averages for the

stations New York, Albany, Philadelphia, and Lakehurst during selected

periods of time in the 1951 hourly record. These show strong similarity

in the weather patterns experienced at each station, with occasional

displacement of a day visible as weather systems cross the distance

between stations. The A to 7 day weather cycles are quite visible.

Figures 9 through 11 present heating and cooling requirements for three

houses during the same periods, together with monthly values for the

full year.* The heating and cooling requirements can be seen to

inversely follow the variation in dry bulb temperatures quite closely

(compare to Figures 6 through 8). The other influences are less

visible.

The authors did not perform a systematic analysis of relationships of

coincident climate elements in the climates of the various stations in

the network. Future work on this may be desirable for a variety of

climatic regions, because it could affect the way the two proposed

extrapolation techniques are used. The question for any given pair

of stations is essentially this: is the climate of the station being

extrapolated to more similar to the overall climate pattern of the source station or is it more similar to occasional periods in the

source's climate when specific weather systems are present at the

* The three houses are described in section 2.3.2. They were used in

the development of the selection model, and represent widely varying

responsiveness to climate.

24
nONTHLV LOADS FOR REAL 19S1 VEARS. FORT flEVER OFFICE DAILV LMDS FOR REAL 1951 VEARS, FORT nEVER

MNTH

DASH*ALBM4V SOLIDOERSEV CITV DOT«ICy VOm DASH3-PNIUOELPHIA Annual

6M

406

zee

-aw

-4W 1—T—'—r—I—r- 13e 135

DAV OF YEAR DASH-ALBANV SOLID-JERSEV CITV DOT^NEU YORK 0ASH3-PHILADELPHIA Transition Season

Me

DAILV LOADS FOR REAL 19S1 VEARS, FORT HEVER 2M6DAILV LOADS FOR REAL 19S1 VEARS. FORT NEVER

DAV OF VEAR DASH-ALBAHY SOLID-JERSEV CITV DOT-NEU YORK DASH3-PHILADELPHIA

Winter

DAY OF VEAR DASH-ALBANY SOLID-JERSEY CITY DOT-NEU YORK DASH3-PHILADELPHIA Summer

21«

Figure 2. Heating and Cooling Requirements for the Fort

Meyer Office Building, Using Real 1951 Years.

25
MONTHLY

ORV BULB S.O. OF DAILY TEMPERATURE AVERAGES DEGREES CELSIUS DEGREES CELSIUS HOtan REAL SN SJ REAL SN SJ

1 -2.79 -2.80 -3.00

2 -2.74 -2.58 -2.78

3 2.06 2.02 2.03

4 8.86 3.82 8.88

5 15.28 15.28 15.27

6 18.73 18.67 18.71

7 22.04 22.07 21.99 2.1

8 19.42 19.46 19.41 2.6

9 15.52 15.55 15.52 3.9

10 10.30 10.30 10.32 4.4 3.9 4.0

11 1.11 1.11 1.07 5.2 4.6 4.0

12 -2.47 -2.47 -2.23 8.4 5.3 5.9

AV6. 8.78 8.79 8.77 4.4 4.0 4.1

WEATHER AVERAGES

DAILY RANGE

OF DRY BULB

DEGREES CELSIUS

FOR ALBANY 1951 DEMPOINT MIND TEMPERATURE SPEED DEGREES CELSIUS METERS/SECOND

5.6 4.8 5.2

6.2 4.7 4.6

3.7 4.4 4.5

3.1 4.9 4.2

4.4 3.4 3.4

3.3 4.0 3.5

2.0 3.9

2.5 2.8

3.8 3.5

REAL

10.4

8.5

8.9 10.7

13.1

11 .2

11 .5

1 2.3

12.2

12.1 9.0

J 0.6

10.9

SN7.7

6.4

e.2

7.9

8.7 8.8

7.5

7.0

7.9

6.6

5.4

7.6

7.3

SJ8.2

7.5

6.9

9.4

10.3

9.3

8.9

8.6

9.3

8.8

6.3

9.0

8.5

REAL -7.0

-7.2

-2.8

2.3

7.1

12.6

16.1 15.0

11.1 6.3

-3.4

-6.5

3.6

SN -8.6 -8

-8.4 -8

-3.1

2.4

7.0

1 2.6

16.1 14.3

1 0.1 5.2

SJ

0

6 -2.9

2.5

7.2

12.8

16.1 14.3

11.0

5.7

REAL

4.57

4.55

4.57

4.16

SN 5.82

5.32

5.45

5.12

4.8 -3.3

8.3 -6.5

2.9 3.4

SJ 4.52

4.37

4.56

4.62

4.63

4.39

3.52

3.70

3.00 4.69 4.23

5.07 3.57

5.22 4.16

5.84 4.37

5.04 4.22

3.30 5.31

3.21 3.95

3.16 4.38

2.33 4.27

3.51

3.76

3.71 3.66

DAYTIME CLOUD COVER

TENTHS

REAL SN 7.66 6.45

6.51 6.27

7.48 7.70

6.64 6 .58 5.94 5.77 5.77 7.15 6. 85 6.88

6.38 6.47 6.36

6.54 6.58

SJ 7.08

6.27

7.57

6.58

6.66

5.69

6.30

7.00 5.87 7.01 6.26 6.64

6.43 6.57

5.82 5.74

6.29 6.61

6.47

6.66

Table 2. Monthly Weather Averages for Albany 1951.

SJ = Selected Composite 1951 using Jersey City 1949-1958 as the source.

SN = Selected Composite 1951 using New York 1949-1958 as the source.

26
»40NTHLY WEATHER AVERAGES

ORV BULB S«0. OF DAILY DAILY RANGE TEMPERATURE AVERAGES OF DRY BULB DEGREES CELSIUS DEGREES CELSIUS DEGREES CELSIUS

FOR LAKEHURST 1951 DEMPOINT WIND TEMPERATURE SPEED DEGREES CELSIUS METERS/SECOND

MONTH REAL

123

4

SN

2.41 2.39

1.88 1.86

5.42 5.39

10.94 10.89

SJ2.42

1 .85 5.43

10.94

5 16.51 16.50 16.52

6 19.88 19.94 19.91

7 23.59 23.64 23.60

8 22.15 22.16 22.13

9 18.84 18.86 18.87

to 13.72 13.68 13.76

11 5.78 5.75 5.74

12 3.4S 3.47 3.54

AV6. 12.05 12.05 12.06

REAL SN SJ 5.0 4.3 3.9

5.5 5.0

3.4 4.3

2.9

3.5

2.2

2.6

3.2

3.5

3.6

3.5

3.6

1.9

2.6

3.2

4.0

5.4

6.9

4.1

3.5

3.3

3.4

3.6

3.5

2.0

2.7

4.0

3.8 3.6

4.8 3.9

3.8 5.4

3.7 3.5

REAL

9.3

10.6

9.5

12.4

12.2

10.1

1 0.7 11.1 12.2

10.2

10.6

10.1 10.8

SN6.6

7.0

7.6

9.1 8.2

7.9

7.0

7.3

8.2

8.2

7.2

7.0

7.6

SJ6.6

7.1 7.2

10.0

8.6

8.3

9.5

7.7

10.4

8.0

7.8

8.5

8.3

REAL SN -2.5 -2.9

-3.1 -3.4

-.0

4.4

9.2

15.6

SJ -3.0

-4.0

-.6

4.2

9.1

.3 4.5

9.3

15.0 15.5

18.2 16.2 18.3

17.8 17.5 17.6

13.8 13.2

10.1 9.1

.9 -.4

.1 -1.5

7. 0 6.6

13.8

9.1.0 -.4

6.6

REAL

5.25

5.01 5.42

5.06

4.55

3.76

3.68

2.96

3.70

3.85 6 4.93 4

SN 5.25

5.07

5.42

5.55

5.52

SJ 5.20

4.92

5.29

4.54

4.85

5.24 3.37

4.12 3.32

4.00

5.62

13

92

3.10

3.68

4.00

4.90

4.63 5.60 4.60

4.40 5.20 4.31

DAYTIME CLOUD COVER TENTHS

REAL SN 6.55 e.53

5.66 6.20

6.14 6.50 6.26

5.78 5.77 5.79

6.52 6.49

7.48 7.58

6.56 6.50 6.45

6.32 6.66 6.28

5.33 5.51 5.51 6.64 6.54 6.72

5.69 6.77 5.67

6.76 6.65 7.07

6.29 6.41 6.36

SJ 6.50

5.67

6.69

7.65

Table 3. Monthly Weather Averages for Lakehurst 1951.

SJ = Selected Composite 1951 using Jersey City 1949-1958 as the source.

SN = Selected Composite 1951 using New York 1949-1958 as the source.

27
MONTHLY MEATHER AVERAGES

S«0. OF OAILY DAILY RANGE

AVERAGES OF DRY BULB

DEGREES CELSIUS DEGREES CELSIUS REAL SN SJ REAL SN SJ 7.5

DRY 8ULB TEMPERATURE

DEGREES CELSIUS

MONTH REAL SN SJ

1 2.71 2.75 2.70 4.7 3.5 2.9 7.5 6.8

2 2.26 2.26 2.21 5.3 3.7 4.0 7.9 6.9

3 9.46 5.4a 5*47 3.2 3.7 3.6 6.7 7.0

4 11.42 11.46 11.41 3.0 4.4 5.2 6.4 6.6 10.5

5 16.77 16.77 16.77 3.3 3.3 3.5 9.7 10.1 9.4

6 20.77 20.76 20.75 3.4 4.6 2.7 6.0 6.1 7 24.42 24.39 24.42 1.9 2.4 2.2 8.0 7.9

6 23.41 23.40 23.45 2.1 2.8 1.7 6.0 8.4

9 20.06 20.05 20.07 3.0 3.4 3.8 7.9 8.4 10.0

10 15.03 15.01 15.01 3.5 2.7 3.8 7.0 7.2 9.0

11 6.50 6.50 6.46 4.9 5.3 4.3 7.7 6.9

12 3.90 3.93 3.65 6.6 5.3 4.2 7.2 7.4

AVe. 12.73 12.73 12.71 3.7 3.6 3.5 7. 6 7.8

FOR NEW YORK 1951 OEWPOINT WIND TEMPERATURE SPEED

DEGREES CELSIUS METERS/SECOND

6.3

7.8

7.3

9.0

10.2

9.6

9.1

7.0

6.8

REAL

-3.6

SN -3.6

-4.2 -4.4

-1 .0 3.6

6.1 14.5

-1.6

3.8

8.2

14.4

17.1 17.1

17.1 17.2

13.0

8.6

-I .0 -2.9

5.8

1 3.0 8.9

-.5

SJ -3.6

-4.4

-1.5

3.6

6.1 14.4

17.1

17.2

13.0

8.6

-.8

6.17

5.22

4.49

3.47

2.3 -2.5

5.8 5.6

REAL SN SJ 6.46 6.40 5.69

6.68 6.91 6.69

7.38

5.66

5.24

5.36

4.34 4.34 3.64

4.41 4.73 3.91 5.06 5.12 4.11 5.98 5.58 4.29

6.11 6.02 5.17

5.70

5.70

7.43

5.76

5.28

4.72

5.60

5.67

5.74

4.90

SJ 6.24

DAYTIME CLOUD COVER

TENTHS

REAL SN 6.36 6.45

6.11 6.12 6.02

6.25 6.31 6.40

5.89 5.69 5.91 5.99 6.10 6.11 6.49 6.53 6.56

6.19 6.06 5.95

6.01 6.21 6.10

5.45 5.67 5.46

6.35 6.26 6.34

5.95 5.94 5.78

6.72 6.69 6.66

6.15 6.19 6*13

Table 4. Monthly Weather Averages for New York 1951.

SJ = Selected Composite 1951 using Jersey City 1949-1958 as the source.

SN = Selected Composite 1951 using New York 1949-1958 as the source.

28
MONTHLY WEATHER AVERAGES FOR PHILADELPHIA 1951 ORy BULB S>0. OF DAILY DAILY RANGE DEWPQINT MIND TEMPERATURE AVERAGES OF DRY BULB TEMPERATURE SPEED DEGREES CELSIUS OEGREI^S CELSIUS DEGREES CELSIUS DEGREES CELSIUS METERS/SECOND

DAYTIME CLOUD COVER TENTHS

MONTH REAL SN SJ RE/L SN S J REAL SN SJ REAL SN SJ REAL SN SJ REAL SN SJ

1 2.23 2.23 2.21 4.3 2.6 4. 1 8.6 5.6 6.6 -3.1 -3.2 -3.1 4 .53 4.66 4.55 6.64 6 .69 6.74

2 2.26 2.18 2.17 5 .4 5.2 4.3 8.9 8.0 7 .9 -4.2 -4.1 -4.1 4.83 6.10 4.87 5.80 5.81 5.75

3 6.11 6.20 6.10 3.4 3.1 3.6 6.5 6.8 6.5 - .6 -.4 -.6 5.60 5.65 5.53 6.50 6.52 6.57

4 11.98 12.11 1 2.00 3.5 3.8 4.2 10.9 8.5 9.4 4.0 4.1 4.4 4.91 5.88 4.9 3 6.34 6.32 6.32

5 17.54 17.54 17.61 3.1 3.0 4. 1 11.3 8.0 11.3 9.1 9.0 9.2 4.25 5.61 4.10 6.42 6.35 6.49

6 21 .58 21 .52 21 .57 3.5 4.3 3.3 9.5 6.0 8.7 16.0 15.9 16.0 3.60 4.73 3.53 7.20 7.21 7.32

7 24.69 24.77 24.65 1.8 1.8 2. 0 9.8 7.2 8.4 16.5 i 6. 6 18.5 3.60 4.02 4.30 6.62 6.63 6.60

8 23.68 23.66 23.70 2.5 2.2 2.9 10.3 7.4 8.9 17.6 1 7.6 17.6 3.14 4.21 3.56 6.31 6.24 6.37

9 20.10 20.1 2 20.06 3.1 2.6 3.0 11.2 8.3 10.2 1 3.5 1 3.5 13.6 3.52 5.32 3.61 5.53 5.53 5.58

10 15.43 15.38 15.43 3.9 3.0 3.9 9.2 7.7 9.2 9.7 9.6 9.7 4.16 3.94 4.21 6.26 6.27 6.17

11 5.77 5.76 5.75 5.0 5.1 4.8 9.3 6.8 8.5 -.3 .2 -.3 4.76 4.75 4.64 6. 09 6.12 6.00

12 3.58 3.47 3.57 6.6 4.3 4.2 8.5 6.4 6.0 -1 .9 -2.0 -1 .7 4 .55 4.98 4.37 6.46 6.57 6.31 AV6. 12.91 12.91 12.90 3.8 3.4 3.7 9. 7 7.4 8.6 6.5 6.6 6.6 4.30 4.99 4.35 6.35 6.37 6.35

Table 5. Ifonthly Weather Averages for Philadelphia 1951.

SJ = Selected Composite 1951 using Jersey City 1949-1958 as the source.

SN = Selected Composite 1951 using New York 1949-1958 as the source.

29
Figure 3. Means and Ranges of Monthly Weather Averages 1949-1958.

30
nONTHLV DAVTinE CLOUD AUERACES FOR REAL 1951 VEARS NONTHLV DAVTinE CLOUD AUERACES FOR NEW VORK 1949-58

1*

,—

_

NONTH

DASH-ALBANV SOLID-LAKEHURST

DOT-NEU VORK DASH3-PHILADELPHIA

10 la nOMTH SOLID-BAXinun AND HIMinUH nONTHLV AUERA6ES

DASHED- !• VEAR AVERAGES

nONTHLV UIND SPEED AUERACES FOR REAL 1951 YEARS RANGE OF nONTHLV UIND SPEED AVERAGES HEU VORK 1949-58

10

n

E

T

E

R 8

SP

ER

S

« E

C0

N

D

le 13

nONTH

OASH-ALRANV SOLID-LAKEHURST

DOT-NEU YORK DASH3-PHILADELPHIA

nONTH

SOLID-HAXinutl AND IIINinuil NONTHLV AUERAGES

DASHED- le YEAR AUERAGES

Figure 4. Means and Ranges of Monthly Weather Averages 1949-1958.

31
Figure 5. Means and Ranges of Monthly Weather Averages 1949-1958.

32
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33
Figure 6. Comparison of Daily Averages for Climate Elements in Winter.

34
DAILY DRV BUll AUCRAGES FOR RCAL 1951 YEARS DAILY yiND SPEED AVERAGES FOR REAL 1951 YEARS

; 1' \:\ 1 %h /4 /

X\ 1

; ; \\ \ - hi m1 \T-/ V \ y, ,•/ 4'<N '' V

,

1 1 1 , . 1 , , , 1 lie lis i2e 135

DAY OF YEAR

DASH-ALBAtfi- SOLID-LAKEHURST

DOT-NEU YORK DASH3-PHILADELPHIA

149 lie 115 lae 125 135 DAY OF YEAR DASH>ALIANY SOLID'LAKEHURST

DOT-NEU YORK DASH3-PHILADCLPHIA

Me

DAILY DEUPOINT AUERAGES FOR REAL 1951 YEARS DAILY DAYTIHE CLOUD AUERAGES FOR REAL 1951 YEARS

DAY OF YEAR DASH-ALBAMY SOLID-LAKEHURST

DOT-NEU YORK DASH3-PHILADELPHIA

DAY OF YEAR DASH'ALBANV SOLID-LAKEHURST

DOT-NEy YORK DASH3-PHILAKLPHIA

14*

Figure 7. Comparison of Daily Averages for Climate Elements in Spring.

35
OAILV DEUPOINT AOERAGES FOR REAL 1951 VEARS MILV OAVTinE CLOUD AVERAGES FOR REAL 19S1 VEARS

8te

DAV OF VEAR DASH-ALBANY SOLIO-LAKEHURST

DOT'NEU VORK 0ASH3-PHILADELPHIA

DAV OF VEAR

DASH'ALIANV SOLID*LAKEHURST

ooT*rcy voMC DASHa-PHiLAoeimiA

ai«

Figure 8. Comparison of Daily Climate Elements in Summer.

36
LOADS. IV nONTN, FOR REAL 1951 RANCH HOUSES DAILY LOADS FOR REAL 1951 VEARS RANCH HOUSES IM

nONTH DASH-ALBANV SOLID-LAKEHURST

DOT-MEU YORK DASH3.PHILflDELPHI

A

-IM

-5« -

lie lis 185 i3e 135

DAV OF YEAR

DASH*ALBANY SOLID-LAKEHURST

DOT-NEU YORK DASH3«PHILADELPHIA

148

Annual Transition Season

DAILY LOADS FOR REAL 1951 YEARS RANCH HOUSES DAILY LOADS FOR REAL 1951 VEARS RANCH HOUSES 6M

8M

DAV OF YEAR DASH-AIBAMY SOLID-LAKEHURST

DOT-NEU YORK DASH3-PHILADELPHIA

-5«

-IM

-15« 285 ate

DAY OF YEAR DASH-ALBANY SOLID-LAKEHURST

DOT-NEU YORK DASH3-PHILADELPHIA

Winter Summer

Figure 9. Heating and Cooling Requirements for Real Year Ranch Houses.

37
LOADS, BV nONTH, FOR REAL 19S1 PASSIVE HOUSES DAILV LOADS FOR REAL 1951 VEARS PASSIVE HOUSES

MONTH DASH-ALBANV SOLIB-LAKEHURST

DOT-NEU VORK DASH3-PHILADELPHIA

Annual

75

5«

25

' 1

-as

-5«

/ V

I \

I \

\ / V

11* 115 12«

-1—I

'—I—III'—I—I—I—I— I I I I iZS 13« 13S 140

DAV OF YEAR DASH-ALBAMV SOLID-LAKEHURST

DOT-NCU VOfiK 0ASH3-PHILAKLPHIA

Transition Season

DAILV LOADS FOR REAL 1951 VEARS PASSIVE HOUSES DAILV LOADS FOR REAL 19SI VEARS PASSIVE HOUSES SM

2M

K

i\

1 >

/> \

1

1 V/ 1 1

/ -

\ >

1 \

3s 4e 45 se

DAV OF VEAR DASH-ALBANV SOLID-LAKEHURST

DOT-NEU VORK DASH3-PHILADELPHIA

-5«

-I— -

-156 -

-2M

ise 185 196 216

DAV OF VEAR DASH-ALBANV SOLID'LAKEHURST

DOT-MEU VORK DASH3-PHILADELPHIA Winter Summer

Figure 10. Heating and Cooling Requirements for Real Year Passive Houses.

38
LOrtDS, 8V nOHTH, FOR REAL 19S1 TOUMHOUSES OAILV LOADS FOR REAL 19S1 YEARS TOUNHOUSES

-at -

-8*

" rr

M

M>;7

\l 1

' y 1

.1 . . 1

1

1 lie lis 129 125 135 MONTH

DASH-ftLBANV SOLID'LAKEHURST

DOT-NEU YORK DASH3'PHILADELPHIA

DAY OF YEAR DASH-ALBANY SOLID-LAKEHURST

DOT-NEU YORK DASH3-PHILADELPHIA

Annual Transition Season

DAILY LOADS FOR REAL 1951 YEARS TOUMHOUSES 5M

DAILY LOADS FOR REAL 19S1 YEARS TOUNHOUSES

2M

DAY OF YEAR DASH-ALBANY SOLID-LAKEHURST

DOT-NEU YORK DASH3-PHILADELPHIA

DAY OF YEAR DASH-ALBANY SOLID-LAKEHURST

DOT-NEU YORK DASH3-PHILADELPHIA

21*

Winter Summer

Figure 11. Heating and Cooling Requirements for Real Year Townhouses.

39
station? For example, if SELECT can find segments from the Jersey

City source tape that ha-ve tVe sane winter means as Albany (e.g.

colder), is a collection of these segments more like Albany's overall

winter climate than "typical" Jersey City winter weather with its mean

adjusted to where it represents Albany's means? Inspection of Albany's

means shows more clouds and less wind then Jersey City's means, which

are in turn cloudier and less windy than Jersey City's coldest segments,

which tend to be clear and windy, as following cold front passage. In

this case Jersey City's typical weather adjusted is somewhat more similar

to Albany's weather than Jersey City's cold weather. In other cases the reverse may be true. A test comparing the two uses of SELECT and ADJUST was done for two sets of stations, from Jersey City to Philadelphia and

from Jersey City to Albany. The results are discussed below in Section

3.3.

3.3 RESULTS

3.3.1 Representative Years Created by SELECT

Table 7 and Figure 12 present computed heating and cooling results

testing the ability of SELECT to create a representative composite

year for a source location. Composite years were created by SELECT for

both New York and Jersey City using their respective ten year tapes as sources and their monthly averages for 1951 as input. The 1951 averages

are used as a substitute for long-term averages. The NBSLD loads program was run for the Fort Meyer Office Building in these two cities,

using the real 1951 years and the two synthetic years.

The residual standard deviations of the monthly cooling requirements

from the desired (real year) values are 0.43 GJ for Jersey City and 0.41

GJ for New York. These values correspond to average monthly cooling

requirements during the cooling season of 3.53 GJ for Jersey City and

3.22 GJ for New York.''" On an annual basis, the real cooling requirement

for Jersey City is 24.70 GJ compared to a predicted value of 23.02 GJ.

The annual values for New York are 22.53 GJ and 22.40 GJ, respectively.

For heating, the residual standard deviations of monthly requirements

from the desired real year values are 0.64 GJ for Jersey City and 0.61

for New York. These values correspond to average monthly heating

requirements during the heating season of 15.19 GJ for Jersey City and

15.04 GJ for New York. On an annual basis, the real heating requirement

for Jersey City is 151.91 GJ compared to a predicted 153.50 GJ. The

annual values for New York are 148.31 GJ and 150.35 GJ, respectively.

The selected year is a fairly good predictor of cooling and heating on an annual basis, if prediction within about 5% on an annual basis is

considered good performance. On a monthly basis, the cooling predicThe ratio of residual standard

coefficient of variation) is a the synthetic and real years.

deviation to population mean (the measure of goodness of fit between

40
tions suffer from the generally low levels of cooling because minor

^ variations in temperature near the balance point are significant in

terms of cooling energy required. In the peak months, the selected

year predicts within 5% of the real year. The heating requirements are predicted very well by the selected year both on an annual and

monthly basis. The greatest errors in the heating requirements may

also be seen to occur in the transition months of April and October.

It should be noted that the representative composite years selected

from the 1949-58 source periods with 1951 averages did not select an excessive number of segments from the 1951 year in the source data pool.

The number of segments for New York is 7 out of 36, and for Jersey City

is 5 out of 36, compared to the average value of 3.6 for all the years.

This supports the use of 1951 to test the ability of SELECT to create

representative years, which normally would use long-term averages not

corresponding directly to those of any one year.

3.3.2 Geographical Extrapolation Using SELECT

SELECT was tested for its ability to produce representative composite

years for geographically removed locations by using the ten-year tapes

from New York City and Jersey City as sources for creating two composite

years for Albany. In addition, a composite Philadelphia was also

tested. As before, the Albany 1951 averages were used to represent

Albany long-term averages. The two Albany composite years were tested

against the real 1951 Albany by comparing the results of NBSLD runs on

each. The results are given in Table 8 and Figures 13a, 13b, 14a, and

14b.

As can be seen, the requirements predicted by the composite year

selected from the New York source data were not accurate. Investigation

of the causes found that New York had differences in climatic characteristics from Albany, preventing SELECT from finding any suitable segments.

These differences were primarily due to an unusually high wind velocity

due to LaGuardia's unprotected location on Flushing Bay, and its small

daily temperature range. These have been presented in Tables 2 and 6.

Because of this, Jersey City, showing more moderate wind speeds and

daily range, was added to the network. Its climatic averages are given

in the example SELECT run. Appendix D.

The energy requirements predicted by the composite year selected from

Jersey City source data proved to be more accurate. The performance

improvement is well represented by the heating and cooling requirements:

216.53 GJ and 13.96 GJ respectively for real Albany, 221.86 GJ and 11.52

GJ for composite Albany selected from Jersey City, and 228.04 GJ and

8.59 GJ for composite Albany selected from New York. Analysis of the

loads program output showed that in the winter, most of the excess heating requirements in the composite years were directly attributable

to infiltration losses, rather than conducted losses. In the transition

seasons and siimmer, most of the shortfall is due to the compressed

distribution of hourly temperatures around the daily averages in the

41
HEATirJG AND COOt-ING LOAOS FOR NEW YORK AND JERSEY CITY OFFICES.

R(JER) SJtJERI R(NY) SN<NY) MONTH HEATING COOLING HEATING COOLING HEATING COOLING HEAT ING COOLING

1 33.151 .000 33.569 .000 32. 339 .000 32.276 .000

2 30. 185 .000 30.829 .000 30.353 .000 31 . 09 8 .000

3 22.996 .000 23.098 .000 24.135 .000 24 . 446 .000

4 6. 858 -.063 8.418 -.195 7.106 -.046 8.630 -.236

5 .659 -1 .426 .989 -1 .268 .826 -1.072 1 .227 -.812

6 .060 -4.268 .188 -4.190 .080 -3.631 .051 -4.658

7 .000 -8.624 .000 -8.773 .000 -7.878 .000 -7.936

6 .000 -7.171 • 000 -6.248 .000 -6.842 . 000 -6.729

9 .344 -2.742 .337 -2.197 .295 -2.550 .265 -2.007

10 3.767 -.406 3.008 -.14S 2.746 -.331 2.044 -.020

11 22*674 .000 22.154 .000 21 .065 .000 20.894 .000

12 31.217 .000 30.907 .000 29.363 .000 29.412 .000

SUBTOTAL 151.91 -24.70 153.50 -23.02 148.31 -22.53 150.35 -22.40

TOTAL 176 .61 176 .32 170 .84 172 .75

Table 7. Comparison of Real and Composite Heating and Cooling

Requirements for the Fort Meyer Office Building in

New York and Jersey City.

R = Real 1951

SJ = Selected Composite 1951 using Jersey City 1949-1958 as the Source.

SN = Selected Composite 1951 using New York 1949-1958 as the Source,

JER = Jersey City 1951 Averages used as Input.

NY = New York 1951 Averages used as Input.

42
Figure 12. Monthly Heating and Cooling Requirements for Jersey City

and New York Real Years Versus Synthetic Years,

Using the Fort Meyer Office Building.

R = Real 1951.

SJ = Selected Composite 1951 using Jersey City ,1949-1958 as the source.

SN = Selected Composite 1951 using New York 1949-1958 as the source. JER = Jersey City 1951 averages used as input. NY = New York 1951 averages used as input. ALB = Albany 1951 averages used as input. PHI = Philadelphia 1951 averages used as input.

A = Adjusted Synthetic 1951 made using prefix year.

43
HEATING AND COOLING LOADS FOR ALBANY OFFICE.

R( ALB) SJ ( ALB ) SN( ALB) SJ ( JER)A(ALB) SJ< ALB) A(ALB)

MONTH HEATING COOLING HEAT I NG COOLING HEAT ING COOLING HEAT ING COOLING HEATING COOLING

1 43.996 .000 45.469 .000 45. 746 .000 44.439 .000 44.692 .000

2 38.514 .000 4 0.210 .000 39.96 3 .000 39.667 .000 36.919 .000

3 31 .981 .000 31 .523 .000 32.557 .000 31 .748 .000 31 .716 .000

4 12.060 -.029 13.005 -.041 13.103 .000 12.125 000 12.297 -•OlS

5 2.849 -1 .264 2.082 - .638 2.402 -.256 2.834 -1 .154 2.734 -1 .249

6 .393 -2.417 .052 -2.173 .880 -2.284 .422 -2.487 .042 -2.365

7 .000 -5.639 .000 -5.942 .0 00 -3.980 .000 -5.685 .000 -S.«52

8 .012 -3.275 .079 -2.127 .001 -1 .553 .126 -3.064 .094 -3.019

9 2.351 -1.125 2.261 -.589 2.1-16 -.521 2.595 -.810 2.516 -1.022

10 10.226 -.161 9.867 -.008 10.971 .000 10.36 7 -.120 1 0.289 -.120

11 33.302 .000 33.716 .000 35.174 .000 33.420 .000 32.644 • 000

12 40.847 .000 43.601 .000 45.128 .000 41 .260 .000 41 .818 .000

SUBTOTAL 216.53 -13.96 221.66 -11.52 226.04 -8.59 219.00 -13.32 217.96 -13.44

TOTAL 230 .49 233 .38 236 .63 232 .32 231. 40

Table 8. Comparison of Real and Synthetic Heating and Cooling

Requirements for the Albany Fort Meyer Office Building.

R = Real 1951

SJ = Selected Composite 1951 using Jersey City 1949-1958 as the source.

SN = Selected Composite 1951 using New York 1949-1958 as the source. JER = Jersey City 1951 averages used as input. NY = New York 1951 averages used as input.

ALB = Albany 1951 averages used as input. PHI = Philadelphia 1951 averages used as input.

A = Adjusted Synthetic 1951 made using prefix year.

44
Figure 13. Monthly Heating Requirements for Albany Real Year

Versus Synthetic Years, Using the Fort Meyer Office

Building.

R = Real 1951.

SJ = Selected Composite 1951 using Jersey City 1949-1958 as the source.

SN = Selected Composite 1951 using New York 1949-1958 as the source. JER = Jersey City 1951 averages used as input.

NY = New York 1951 averages used as input.

ALB = Albany 1951 averages used as input.

PHI = Philadelphia 1951 averages used as input.

A = Adjusted Synthetic 1951 made using prefix year.

45
nONTHLY COOLING LOftDS FOR ALBftNV OFFICE NONTNLV COOLINC LOADS FOR ALIANV OFFICE

-4

-6 -t -5

6I6<U0UL£S SJ(ALI)A(ALI) VEAI

((•ACTUAL MtLUES SOLID-HOKL fiR • OS RESIBUM. STMOMD OCVIATIOM • .IN

GI6AJ0ULES SJ(JER)A(ALI) VEAR X-ACTIWL UALUES SOLID-TODEL OR • QS RESIDUAL STANDARD DEUIATION • .153 14C 14D

Figure 14. Monthly Cooling Requirements for Albany Real Year versus Synthetic Years, Using the Fort Meyer Office

Building.

R = Real 1951.

SJ = Selected Composite 1951 using Jersey City 1949-1958 as the source.

SN = Selected Composite 1951 using New York 1949-1958 as the source. JER = Jersey City 1951 averages used as input.

NY = New York 1951 averages used as input. ALB = Albany 1951 averages used as input. PHI = Philadelphia 1951 averages used as input.

A = Adjusted Synthetic 1951 made using prefix year.

46
composite years. The daily range for real Albany is 10.9°C, 8.5°C for

composite Albany selected from Jersey City, and 7.3°C for composite

Albany selected from New York. The averages for the three years are basically the same, and the differences in the other climatic averages

between the years are also small. Because very few Albany days have

the majority of their hours in the cooling region (the averages for

July is 7.6 hours per day) most of the cooling occurs in the hottest

hours of the day. With a restricted range, the duration and severity

of this daily cooling period is diminished. The number of annual cooling hours for real Albany is 752, for composite Albany selected from

Jersey City 608, and for composite Albany selected from New York 512.

The composite year selected for Philadelphia from Jersey City source data

showed a similar relationship between daily range and cooling hours. The

number of annual cooling hours for real Philadelphia is 1185 and for the

composite Philadelphia selected from Jersey City is 1121, corresponding

to annual daily range averages of 9.7°C and 8.6°C, and total cooling

requirements of 28.73 GJ and 26.11 GJ. The daily range was the only

major discrepancy found in Philadelphia composite year climate averages.

The total heating requirements are less sensitive to daily range because

most heating in Philadelphia takes place at average temperatures far

below the balance point, and are better predicted: 142.52 GJ for real

Philadelphia and 144.77 GJ for the composite Philadelphia selected from

Jersey City.

SELECT alone did not perform satisfactorily, particularly in cooling

predictions, in the geographical extrapolation from New York and Jersey

City to Albany. This is not surprising in view of the large distances

and climatic differences between the source and remote stations. The

tests pointed up the importance of climatic similarity between source

and remote station, as shown by the comparison of New York- and Jersey

City-based composite years for Albany. The greater climatic similarity

between Jersey City and Philadelphia allowed a more effective extrapolation.

3.3.3 Geographical Extrapolation Using ADJUST

Table 9 and Figures 15a, 15b, 16a, and 16b give the monthly heating and

cooling requirements for the synthetic years created for 1951 Philadelphia from 1951 Jersey City and 1951 New York. These results show the

ability of the adjustment technique to extrapolate hourly weather data

for a real period of time. The performance is excellent, because the

similarity of weather patterns in real time between nearby stations

means that hour-by-hour adjustment of the source weather will produce a synthetic year with not only the identical means as the real year, but

the same sequences of weather events as well.

The residual standard deviation of predicted monthly heating and cooling

requirements about the real requirements are 0.095 GJ and 0.134 GJ for

the adjusted Jersey City year and 0.141 GJ and 0.134 for the adjusted

New York year. The 0.134 GJ residual standard deviations are about

47
HEATING AND COOLING LOADS FOR PHI LAOEL PH lAOFF I CE. R(PHI

i

R(JER) A(PHI

)

P<NY)A(PHI) SJ ( JER) A<PH1

)

SJ(PHI )A(PHt) MONTH HEAT ING COOLING HEATING COOLING HEATING COOLING HEATING COOLING HEATING COOLING

1 31 .674 .000 31 .840 .000 31.947 .000 32.096 .000 32.412 .000

2 28.710 .000 28.743 .000 28.809 .000 29.095 .000 29.070 .000

3 20.S41 .000 20.992 .000 20.997 .000 20.761 .000 21.063 • 000

4 6.653 -.236 6.466 -.210 6.452 -.263 6.793 -.138 6.672 -.116

S .590 -1.565 .504 -1 .647 .413 -1.633 .665 -1.229 1.119 -1.416

6 .047 -5.316 .029 -5.257 .048 -5.345 . 063 -4.998 .002 -4.906

7 .000 -9.355 .000 -9.313 .000 -9.234 . 000 -9.334 .000 -9.300

8 .000 -8.260 .000 -8.192 .000 -8.174 .000 -8.047 .000 -8.214

9 .309 -3.139 .270 -3.375 .304 -3.357 .160 -3.114 .086 -2.847

10 2.548 -.864 2.580 -.636 2.576 -.640 2.330 -.567 2.707 -.465

11 21 .854 .000 21 .968 .000 22.046 .000 21 .948 .000 21 .825 .000

12 29.194 .000 29.228 .000 29.238 .000 28. 891 .000 29.446 .000

SUeTOTAL 142.52 -28.73 142.62 -28.63 142.83 -28.65 142.81 -27.43 144.40 -27.26

TOTAL 171 .25 171 .25 171 . 48 170 .24 171 .66

Table 9. Comparison of Real and Synthetic Heating and Cooling

Requirements for the Philadelphia Fort Meyer Office

Building.

R = Real 1951.

SJ = Selected Composite 1951 using Jersey City 1949-1958 as the s JER = Jersey City 1951 averages used as input.

PHI = Philadelphia 1951 averages used as input.

A = Adjusted Year made using prefix year.

48
Figure 15. Monthly Heating Requirements for Philadelphia Real Year versus Synthetic Years, Using the Fort Meyer Office

Building.

R = Real 1951.

SJ = Selected Composite 1951 using Jersey City 1949-1958 as the source. SN = Selected Composite 1951 using New York 1949-1958 as the source. JER = Jersey City 1951 averages used as input.

NY = New York 1951 averages used as input. ALB = Albany 1951 averages used as input. PHI = Philadelphia 1951 averages used as input.

A = Adjusted Synthetic 1951 made using prefix year.

49
BOHTHLV COOLINC L0«OS FOR PHILflDeLPHIA OFFICE nONTHLV COOLING LOADS FOR PHILAKLPHIA OFTICC

-6

-1* -8 -6 eiCMOULES SJ(PHI)A(PHI) VEAR X-ACTUAL WALUCS SOUID-HODCL OR • OS RESIDUAL STANDARD DEUIATIOH • .255 16C

GieAJOULES SJ(JER)A(PHI> YEAR X^ACTUAL UALUES SOLID-nODEL OR • OS RESIDUAL STANDARD OEUIATIOH • .226 16D

Figure 16. Monthly Cooling Requirements for Philadelphia Real Year

versus Synthetic Years, Using the Fort Meyer Office

Building

.

R = Real 1951.

SJ = Selected Composite 1951 using Jersey City 1949-1958 as the source.

SN = Selected Composite 1951 using New York 1949-1958 as the source. JER = Jersey City 1951 averages used as input.

NY = New York 1951 averages used as Input. ALB = Albany 1951 averages used as input.

PHI = Philadelphia 1951 averages used as input.

A •= Adjusted Synthetic 1951 made using prefix year.

50
Philadelphia's average monthly cooling load of 4.10 GJ. The 0.095 and

0.141 GJ residual standard deviations are about Philadelphia's average

monthly heating load of 14.25 GJ. On an annual basis the real heating

requirement for Philadelphia is 142.52 GJ with the adjusted Jersey City

predictions 142.62 GJ and adjusted New York 142.83 GJ. The corresponding values for cooling requirements are 28.73 GJ, 28.63 GJ and 28.65 GJ.

3.3.4 Geographical Extrapolation Using Both SELECT and ADJUST

The first tests are on using SELECT for geographic extrapolation by

creating a representative composite year at the remote location,-

and then using ADJUST so that the monthly averages are the same as the input averages. The composite 1951 Philadelphia and Albany years

selected from Jersey City source data were adjusted to the real 1951

Philadelphia and Albany monthly averages. The predicted heating and

cooling requirements are presented in Tables 8 and 9, and in Figures

13c, 14c, 15c, and 16c.

For Philadelphia, the ADJUST program improved the total heating and

cooling requirement prediction from 144.77 GJ and 26.11 GJ for the

composite year selected from Jersey City (discussed above), to 144.40 GJ

and 27.26 GJ, with the real requirements being 142.52 GJ and 28.73 GJ.

For Albany, the improvements in heating and cooling requirement prediction are from 221.86 GJ and 11.52 GJ for the Albany composite selected

from Jersey City, also discussed above, to 217.96 GJ and 13.44 GJ, with

the real requirements being 216.53 GJ and 13.96 GJ. The residual standard deviations of the predicted monthly requirements about the real

monthly requirements showed the following improvements from the composite

to the adjusted composite: from 1.158 GJ to 0.437 GJ for heating and

from 0.553 GJ to 0.108 GJ for cooling. The average monthly heating is

19.68 GJ and average monthly cooling is 1.99 GJ.

It might be noted that ADJUST made the following improvements to the

Albany composite year selected from New York, which as discussed above

does not predict well: from 228.04 GJ and 8.59 GJ for the composite

year, to 217.60 GJ and 12.96 GJ. The rest of the New York results have

not been shown in tables because it is felt that although the results as good as those from Jersey City, Jersey City is a climatologically a better source for Albany climate data.

The second set of tests of SELECT and ADJUST combined use SELECT to

create a representative composite year at the source location, and use ADJUST to make a year with monthly averages the same as those of the

remote location. ADJUST thus performs the extrapolation. The representative composite year for Jersey City was adjusted to both Philadelphia and Albany using their 1951 monthly averages. The predicted heating

and cooling requirements are presented in Tables 8 and 9 and Figures 13d,

14d, 15d, and 16d.

For Philadelphia, heating and cooling requirements using this second

method are 142.81 GJ and 27.43 GJ. For Albany, they are 219.00 GJ and

51
13.32 GJ. The residual standard deviations for Philadelphia are 0.236

GJ heating and 0.226 GJ for cooling, corresponding to monthly averages

of 14.25 GJ and 4.10 GJ. The residual standard deviations for Albany

are 0.411 GJ heating and 0.153 GJ cooling, corresponding to monthly

averages of 19.68 GJ and 1.99 GJ.

The Philadelphia predictions using the second method (ADJUST performing

the extrapolation) are slightly better than those of the first method

(SELECT performing the extrapolation). This can be seen in both the

predicted heating and cooling requirements, and in the residual standard

deviations, which for the first method are 0.333 GJ for heating, and

0.255 GJ for cooling. The Albany predictions using the second method

are slightly worse than those of the first method. This can been seen

in the predicted heating and cooling requirements, and in the residual

standard deviations for cooling. For heating, the residual standard

deviations go slightly the other way.

Although Philadelphia had better results extrapolating a composite

source year with ADJUST, the small magnitudes of the differences do not

permit conclusions about the cllmatologlcal causes for the differences.

In Albany, extrapolating with SELECT produced slightly better results

over the whole year. The initial analysis of Jersey City and Albany

climate characteristics during cold weather (section 3.2) did suggest

that extrapolating with ADJUST would be better. This is borne out in

the fit of energy values for the coldest months, but the differences

again are not sufficient to warrant a conclusion.

It may be concluded that the use of ADJUST together with SELECT substantially improves the predictive ability of representative years that

have been extrapolated to remote locations. It is not possible to

conclude from these tests which combinations of SELECT and ADJUST yield

better extrapolated weather years.

Two untested uses of SELECT and ADJUST are: the Improvement of composite years representative of the source location by ADJUST using as input the averages of the source location, and the use of SELECT and

ADJUST to create specific years for which hourly data are missing using

as a source other years available from the same location and as input

monthly averages available from those years. It is probable that these uses of the two techniques will yield successful results since SELECT

alone has been shown to produce accurate representative years at the source location and the additional Improvement required of ADJUST is

likely to be small.

Facing page:

Anemometer and Stevenson thermometer

measure wind speed and temperature

respectively near solar collector

array .

52
4. SYNTHESIZING CLIMATE DATA: CAVEATS

Climate data should be synthesized with caution. Although this report

has presented some successful examples of composite and synthetic years

representing the energy usage both of long-term climate and of geographically separate locations, it is also possible to create unsuccessful

ones. Success or failure would depend on: (1) the effectiveness of the

synthesis technique itself, (2) the appropriateness and quality of the

input data, and (3) in the case of geographical extrapolation, the

53
cliraatological homogeneity between source and remote station. Taking

the proposed S3mthesis techniques as given, the following precautions

should be adopted in their use.

° Input data

Care must be taken to assure that the monthly input averages for all climate variables come from the same period of

record, or that if there are different periods, the effect

is insignificant. The period 1941-70 is the current standard for normals published by NOAA, but many stations have

shorter periods of record, and some published sources such

as the Climatic Atlas use earlier periods for their monthly

summarized values. Since climate is variable over time,

including periods as long-term as 30 years, the use of

different periods for different climate elements could lead

to distorted joint distributions of climate elements. If

the use of different data periods cannot be avoided, the

periods should be compared using elements for which there

are averages available from each period.

The user should also check whether the weather recording

station has moved during the period of record. NOAA will

make adjustments for the changes in the data caused by such

moves, but not all records have such corrections.

° Climatological homogeneity

The synthesis techniques described in this paper do not

require a statistically-based understanding of the climate

patterns in a region in order to synthesize hourly climate

data. The use of fairly long segments of actual hourly

data in the selection technique, and of systematic adjustment of real weather data in the adjustment technique,

retains the naturally-occurring climate pattern, providing

the differences between the remote station's input averages and the source's averages are not excessive. The user

must, however, avoid extrapolations with excessive adjustment, and must determine whether the remote station does in

fact have climatic patterns similar to the source station.

The authors do not now know the limits to successful use of the adjustment technique, given similar climatic patterns for the source and

remote stations. The Jersey City-to-Albany extrapolation produced a synthetic year giving very successful annual energy prediction, although

the distance and climatic differences between stations were considerably

greater than the technique had been intended for. The limits to such

adjustment should be tested in the future.

The critical issue for the user of these techniques is determining

whether the climatic patterns in the two locations are similar. The

New York/La Guardia-to-Albany extrapolation showed that differences in

54
climatic patterns could have a noticeable effect on the success of the

extrapolation. New York's climate, with decreased daily range and

higher wind, could not be made into Albany (using SELECT only), as successfully as Jersey City's climate, with more inland values of

daily range and wind.

Some of the differences between New York/La Guardia and Jersey City

may be detected from the data printout by examining the relative magnitudes of monthly means. However, the user should at least qualitatively

understand the daily characteristics of the climates on the two sites.

Within-day subtleties such as early morning fog, afternoon cumulus

activity, sea and lake breezes, and valley winds must be considered in

the selection of source sites. In addition, the user should know whether

the locations experience periodically recurring events that could influ- ence energy consumption. Various regional examples might be Chinook or Santa Ana winds, unusual frontal passages, and lake or ocean fogs. The

user can obtain such qualitative information from a variety of sources,

such as flight weather services at airports, agricultural extension

services, and local residents. Stations situated near mountainous

terrain or discontinuities such as coasts should be examined with

particular care.

If there are remaining questions about the nature or extent of differences between stations, the user is strongly recommended to retain a consulting meteorologist to analyze the climatic differences. Although

the energy implications of the climatic differences will probably not be

readily apparent, the analysis may suggest ways to use the techniques

that will yield the most realistic synthetic climate data. For example,

analysis could suggest the most effective combination of SELECT and

ADJUST discussed in Section 3.2 and 3.3. It could also form the basis

for deciding whether to use a combination of sources bracketing the

remote station to mix the types of data selected.

Once a synthetic year is created, the user can inspect and analyze the

hourly values to see whether daily and sub-monthly climatic patterns

are similar; but if they are not, there is nothing he can do about it

with unmodified versions of the programs other than change the location

of the source data and create another synthetic year.

55
Facing page:

Local wind direction indicator:

weathervane, Governor' s Palace,

Williamsburgf Virginia,

56
5. CONCLUSIONS

Two techniques have been developed and tested for creating representative weather data for the hour-by-hour computation of building energy

requirements. The data, in the format of NBSLD-decoded hourly weather

tapes, may be sjmthesized at a source location for which there is a long record of hourly observations on tape or at a remote location for

which there are only monthly averaged data available. The techniques

may be used for simulations of probable future building energy use (composite and/or synthetic years representative of long-term climate), or

57
to represent specific periods of time, as used for simulations to match

experimental measurements being made on buildings in locations where

only climatic averages are being recorded.

The two techniques are described and their listings appended. The

selection technique embodied in SELECT chooses unchanged segments of

actual hourly weather data from the source and links them into composite years. The procedure is based on an empirical function weighting

the importance of various climate variables on the energy consumption

of a range of envelope-dominated buildings, and upon determination of

several issues, particularly the effects of segment length and monthly

temperature distribution on energy prediction performance.

The adjustment technique embodied in ADJUST adjusts the hourly values

of each climate element in a source tape to make the monthly means of

the synthetic tape match the input means. It, unlike SELECT, is capable

of incorporating a measure of the hourly temperature range around the

daily mean (the daily range). This statistic is shown to be an important influence on energy use, and it has a strong effect on the extent

of climatological similarity between stations.

The effectiveness of the two techniques was tested for several of their

applications in a series of tests using NBSLD simulations of a onestory office building that is sensitive to climatic variation through

energy exchange through the building envelope. The techniques were

used to create composite and synthetic weather tapes for Albany, Philadelphia, Lakehurst, Jersey City, and New York/LaGuardia, with the

latter two serving as source stations with data from the period 1949-

58.

The first tests provide an indication of SELECT 's effectiveness at

creating representative years. Composite years were created for both

New York and Jersey City from their own source data and monthly input

averages from 1951. The composite years for both stations predicted

annual cooling within 7 percent of the 1951 year, and heating within

1.5 percent. On a monthly basis, the residual standard deviation of

monthly cooling predictions were within 13% of the real monthly mean

cooling values, and the heating values within 4%. These results suggest

that SELECT may be an effective way to create a year conforming to a set

of input averages. Since the input averages from a single year are not

dissimilar to those of a long-term record, it is concluded that SELECT

can be used to produce composite years representative of a long period

of record. However, the results' observed inaccuracies also suggest

that, unless there is a strong reason to retain unmodified hourly data

in the composite year, the ADJUST program should be applied to the

composite year to make its monthly climatic means match long-term means

exactly. The performance of ADJUST in the extrapolation tests showed

that it is capable of strongly improving representative composite years

created by SELECT. This was not tested in the case of the source cities

Jersey City and New York, however.

58
In the second tests, SELECT was used to create, or "extrapolate", composite years for three remote stations using source data from New York and

Jersey City. Climatological differences between New York and Albany

caused poor prediction in the New York-to-Albany extrapolation: 39 percent low in cooling and 5 percent high in heating. Although Jersey City

and New York are equally far from Albany, the lesser climatological differences cause significantly lower prediction errors: 17 percent low

for cooling and 2 percent high for heating. The results for the Jersey

City-to-Philadelphia extrapolations, 9 percent low for cooling and 1.5

percent high for heating, reflect Philadelphia's closer location and

more similar climate.

The performance of each extrapolation was improved by using ADJUST on

the extrapolated values. In the fourth set of tests, the adjusted

SELECT extrapolations for Albany-from-New York predicted 8 percent low

for cooling and 0.5 percent high for heating. Adjustment of the better

extrapolation Albany-from-Jersey City resulted in a prediction 4 percent

low in cooling and 1.0 percent high in heating. The improvement on the

SELECT extrapolations to Philadelphia were from 9 percent to 5 percent

low for cooling, and from 1.5 percent to 1.0 percent high for heating.

These results are considered very satisfactory considering the very

large distance and climatological differences between the stations.

The third and fourth sets of tests examined the effectiveness of ADJUST

at extrapolating both real and composite weather data. The 'real period'

extrapolations used 1951 averages from Philadelphia as input to adjust

Jersey City and New York 1951 years. The results are considered

extremely good, all within 1 percent on an annual basis; and on a monthly basis, both sets of residual standard deviations of predicted

monthly heating and cooling values around the real monthly values are within 3 percent of the mean monthly cooling value, and within 1 percent

of the mean monthly heating value. The good results are due to the dayto-day similarity between the source year and the real year providing

the input data. This is exactly the situation in experimental energy

evaluations where climatic averages are being collected on site and the

building being measured is experiencing a climate different from the source station but with basically the same day-to-day sequence of weather

events. The subsequent hour-by-hour simulation of such a building with

an adjusted real period of weather data from the nearest source is

likely to be quite realistic.

As expected, the adjustment of non-extrapolated SELECT representative

composite years from the New York and Jersey City sources yielded some- what less excellent results: for both Albany and Philadelphia from the

Jersey City representative year, they are within 5 percent for annual

cooling and 1 percent for annual heating. These values are very close

to those produced by adjusting the SELECT-extrapolated Albany and Philadelphia composite years; no conclusions can be drawn about which is the

better combination for producing representative years for remote locations. Both methods are considered to yield very satisfactory extrapolated weather tapes based on the results of these tests.

59
The report includes caveats to the users of these techniques, recommending that climatic characteristics of source and remote stations

be assessed before data is extrapolated. The differences in diurnal

climatic patterns are important and should be examined. The sources

and recording periods of climatological data should also be checked to

assure uniformity.

The techniques should also be examined for their effectiveness in

different climatic regions of the country before great reliance is

placed on them outside the Northeast.

60
REFERENCES

1. Environmental Data Service, National Oceanographic and Atmospheric

Administration (NOAA), Monthly Normals of Temperature, Precipitation, and Heating and Cooling Degree Days 1941-70 , Climatography

of the U.S. No. 81 (By State), National Climatic Center, Asheville,

N.C., 1973.

2. R. M. Whiting, Standard Deviation of Monthly Average Temperature in

the United States , NOAA Technical Report EDS 3, Environmental Data

Service, Washington, D.C., 1978.

3. National Climatic Center, Tape Reference Manual "Test Reference

Year ," National Climatic Center, Asheville, N.C., 1976. Also

published in the ASHRAE Journal, February 1977, p. 47.

4. I. J. Hall, R. R. Prairie, H. F. Anderson, and E. C. Boes, Generation of Typical Meteorological Years for 26 SOLMET Stations , Sandia

Laboratory Report, N. M. 87185, 1979.

5. L. W. Crow, Summary Description of Typical Year Weather Data , Chicago Midway Airport , ASHRAE Project Report RP-lOO, Report 80,

February 1970.

6. National Climatic Center, Selective Guide to Climatic Data Sources , Key to Meteorological Records Documentation No. 4.11, National

Climatic Center, Asheville, N.C., 1969.

7. U. S. Navy, Guide to Standard Weather Summaries and Climatic

Services , NAVAIR 50-1C-534, National Climatic Center, Asheville,

N.C., 1978.

8. National Climatic Center. Climates of the States , Climatography of

the U. S. No. 60, (by state). National Climatic Center, Asheville,

N.C., (a continuing series).

9. Environmental Data Service, Climatic Atlas of the United States , Environmental Data Service, NOAA, Washington, D.C., 1968.

10. Engineering Weather Data , AFM 88-29, TM5-785, NAVFAC P-89, Departments of the Air Force, the Army, and the Navy, Washington, D.C.

20330, 1978.

11. American Society of Heating, Refrigerating and Air Conditioning

Engineers, ASHRAE Handbook - 1977 Fundamentals , ASHRAE, 1977.

12. L. Degelman, A Weather Simulation Model for Building Energy

Analysis, Computer-Aided Design and Simulation Laboratory, Pennsylvania. State University, University Park, Pennsylvania, 1974.

61
13. D. Nail and E. Arens, Climate Data Abbreviation for the Computerized Calculation of Heating and Cooling Requirements In Buildings , NBSIR 78-1525, National Bureau of Standards, 1978.

14. American Society of Heating, Refrigerating and Air Conditioning

Engineers, ASHRAE Standard 90-75, Energy Conservation in New Building Design , ASHRAE, 1975.

15. S. R. Hastings, Three Proposed Typical House Designs for Energy

Conservation Research , NBSIR 77-1309, National Bureau of Standards,

1977.

16. T. Kusuda, NBSLD, The Computer Program for Heating and Cooling

Loads in Buildings , Building Science Series 69, National Bureau of

Standards, 1976.

17. R. J. List, Smithsonian Meteorological Tables, Sixth Revised

Edition , Smithsonian Institution Press, 1971.

18. K. Kimura, and D.G. Stephenson, Solar Radiation on Cloudy Days , ASHRAE TRANSACTIONS 1, 1969.

19. S. C. Diamond, et al., DOE-2 Program Manual , U.S. Department of

Energy, Report LA-7688-M, February 1979.

20. E.C. Barrett, and C.K. Grant, Relations Between Frequency Distributions of Cloud Over the United Kingdom Based on Conventional

Observations and Imagery from Landsat 2. Weather , Vol. 34, No. 11,

November 1979, pp. 416-424.

62
APPENDIX A: Listing of the Main Program of the FORTRAN Program SELECT.

This Program Requires the Following Subroutines and Functions;

Subroutine DAYW

Subroutine DEWPNT

Subroutine ORDER

Subroutine SLYEAR

Subroutine SUNNY

Function DPF

Function PVSF

Listed in APPENDIX C.

Al
Synopsis of FORTRAN Program SELECT

Read in 3 to 10 years of decoded weather

files, compute daily averages, and store

in a FORTRAN DEFINE FILE.

Compute monthly averages for weather

variables, and print out to user.

Read input monthly averages.

Computer averages appropriate for the

selection model, and determine the

order of segment selection.

By month:

Select first segment by selection

model to match input averages.

Select second-selected segment by

monthly temperature deviation.

Select third-selected segment, so that

the averages of the three segments match

the input averages as closely as possible

on this attempt.

Reselect the second-selected segment

so that the averages of the three

segments approach the input averages

more closely.

Reselect the third-selected segment

so that the averages of the three

segments approach the input averages

more closely.

Print out summary of selected composite year.

Write out composite year weather file in

decoded format for use by loads program

or refinement by ADJUST.

A2
l: G

2: C SELECTED YEAR

3: G

4: C MAIN ROUTINE OF A PROGRAM TO CREATE FULL YEAR VEATHER TAPES

5: C FOR AREAS WHERE HOURLY DATA ARE NEEDED AND ONLY MONTHLY DATA ARE

6: C AVAILABLE. WEATHER DATA MUST BE IN NBSLD-DECODED FORMAT, AND

7: C MAY BE ON TAPES OR IN FILES. THE SELECTED YEAR IS CREATED BY

8: G SELECTING 9, 10, OR 11 DAY SEGMENTS FROM AN INPUT SET OF YEARS.

9: G UP TO TEN TAPES MAY BE USED TO DRAW ON TO CREATE A DEFINE FILE

10: C THREE TO TEN YEARS LONG. THE SEGMENTS ARE CHOSEN FOR THEIR FIT

11: C WITH INPUT MONTHLY AVERAGES. UP TO FOUR SETS OF MONTHLY

12: C AVERAGES MAY BE ENTERED WITH A FULL YEAR TAPE MADE FOR EACH.

13: G USE LOGICAL UNITS 15—24 FOR THE INPUT TAPES STARTING WITH 15. 14: G USE LOGICAL UNITS 11 —14 FOR OUTPUT SELECTED YEARS STARTING WITH 11.

15: G THE FIT OF THE SEGMENTS IS BY USE OF A REGRESSION FORMULA, BASED

16: G ON THE ENERGY REQUIREMENTS OF THREE WIDELY DIFFERING BUILDINGS,

17: G WHICH USES A WEIGHTED SUM OF THE AVERAGE DRY BULB TEMPERATURE,

18: G DAYTIME CLOUD COVER, MOISTURE RATIO. AND WIND-SPEED- DRY-BULB

19: C CROSS-PRODUCT TO RANK THE SEGMENTS. THE RANK IS AN ESTIMATE OF

20: C THE DIFFERENCE IN LOADS. IN MEGAJOULES PER DAY, BETWEEN THE

21: C SEGMENT AND ONE WITH THE INPUT MONTHLY AVERAGES, AS AVERAGED OVER THE

22: C TOREE BUILDINGS.

23: C TEE SEGMENTS FOR A MONTH ARE SELECTED FROM THE SAME MONTH OR

24: C THE ADJACENT MONTH ON EITHER SIDE.

25: G A TEMPORARY FILE MUST BE ASSIGNED TO HOLD A DEFINE FILE (3700,226,,).

26: G ASSIGN IT TO LOGICAL UNIT 10. 27: C SUBROUTINES:

28: G DAYW (MN, IDAY,DBTP,DRNG,CCAD,CCT,DDHT,DDCL,DEWP,WBTP,WNSP,

29: G IYEAR,NUMDAY, INITYR.LOGUNI ,DDBASH,DDBASC.BAPR)

30: C READS DAY NUMDAY FROM THE TAPE ASSIGNED TO LOGUNI,

31: C WRITES IT INTO OUR DEFINE FILE AND FINDS DAILY AVERAGES

32: C AND OTHER VALUES AS ARE NEEDED IN THE MAIN PROGRAM.

33: G ORDER ( RANKSP , ISEGDN, NUNBYR) ORDERS THE VALUES IN ARRAY RANKSP

34: C IN INCREASING ORDER, PLACES SUBSCRIPTS OF THEIR ORIGINAL

35: G POSITIONS IN ARRAY ISEGDN. NUMBYR IS THE NUMBER OF ENTRIES

36: C IN RANKSP.

37: G SLYEAR ( ICDT, LSEG, LOGUNI , I YRARR, RANKCH) WRITES OUT A SELECTED

38: C YEAR INTO LOGICAL UNIT LOGUNI USING THE STARTING DAYS

39: C IN ARRAY ICDT AND THE CORRESPONDING LENGTHS IN ARRAY

40: C LSEG TO FIND THE CHOSEN SEGMENTS. THE YEAR AND CITY

41: G CODE FOR THE SELECTED YEAR MUST BE ENTERED BY THE USER.

42: G ALSO WRITES THE YEAR AND RANK OF THE CHOSEN SEGMENTS

43: G USING lYRARR AND RANKCH.

44: G SUNNY ( RLATD, RLONG, TZN)

45: G FINDS SUNRISE AND DAY LENGTH VALUES ON A MONTHLY

46: G BASIS, l^EN GIVEN LATITUDE, LONGITUDE, AND TIME ZONE.

47: C DEWPNT ( DBTP , WBTP, BPR, DEWP)

48: C FINDS DEWPOINT WHEN SENT DRY BULB, WET BULB, AND

49: C BAROMETRIC PRESSURE.

50: G FUNCTION:

51: C PVSF (DEWP IN OR DEWPSP) INPUTS A DEWPOINT TEMPERATURE AND RETURNS THE

52: G PARTIAL VAPOR PRESSURE OF WATER IN MOIST AIR, KILOPASCALS.

53: G DFF (VP)

54: G CALCULATES DEWPOINT FOR A GIVEN VAPOR PRESSURE.

A3
55: C VARIABLES:

56! C CCAD: THE AVERAGE DAYTIME CLOUD COVER, FOUIH) IN DAYW, TENTHS.

57: C CCT: THE AVERAGE TOTAL CLOUD COVER, FOUND IN DAYW, TENTHS.

53: C DBTP: THE AVERAGE DRY BULB TEMPERATURE, FOUND IN DAYW, DEC. C. 59: C DUNG: THE DAILY TEMPERATURE RANGE, FOUND IN DAYW, DEG. C. 60: C DEWP: THE DEWPOINT TEMPERATURE, FOUND IN DAYW, DEG. C. 61: C WBTP: THE WET BULB TEMPERATURE, FOUND IN DAYW, DEG. C. 62: C BAPR: THE BAROMETRIC PRESSURE, FOUND IN DAYW, KILOPASCALS.

63: C WNSP: THE WIND SPEED, FOUND IN DAYW, M PER S. 64: C DDHT: DEGREE DAYS HEATING BASED ON DDBASH.

65: C DDCL: DEGREE DAYS COOLING BASED ON DDBASC.

66: C DADBTP(DAY): THE STORED DBTP VALUE FOR EACH DAY.

67: C DBTPIN(MN): THE INPUT DBTP VALUE TO BIAS OUR CHOICE OF

68: C SEGMENTS, IN DEG. C. OR F. 69: C ZMDBTP: STORES A DBTP IN VALUE SO WE CAN WORK WITH IT MORE EASILY.

70! C DBTPSL( YEAR. MN, LENGTH) : THE AVERAGE DBTP FOR OUR SELECTED SEGMENTS.

71: C DBTPSP( SEGMENT) : THE AVERAGE DBTP FOR THE SEGMENTS WE ARE CHOOSING

72: C FROM FOR A MONTH.

73: C DBTPXj A SUMMING VARIABLE TO HELP FIND THE AVERAGES FOR THE SEGMENTS.

74: C YDBTP(MN): THE AVERAGE DBTP FOR A MONTH FROM OUR INPUT SET.

75: C DBTPMN(N,MN) : THE MONTHLY AVERAGE DBTP FOR THE NTH INPUT YEAR.

76: C SLDBTP: THE AVERAGE DBTP FOR OUR SELECTED YEAR, DONE FOR EACH MONTH.

77: C DADEWP(DAY): THE STORED DEWP VALUE FOR EACH DAY.

78: C DEWPIN(MN): THE INPUT DEWP VALUE TO BIAS OUR CHOICE OF SEGMENTS.

79: C ZMWAMR: STORES A MOISTURE RATIO VALUE SO WE CAN WORK WITH IT MORE EASILY

80: G COMPUTED FROM DEWP IN AND BAPRIN VALUES.

81: C DEWSL( YEAR, MN, LENGTH) : THE AVERAGE DEWP FOR OUR SELECTED SEGMENTS.

82: C DEWPSP( SEGMENT) : THE AVERAGE DEWP FOR THE SEGMENTS WE ARE CHOOSING

83: C FROM FOR A MONTH.

84: C DEWPX: A SUMMING VARIABLE TO HELP FIND THE AVERAGES FOR THE SEGMENTS.

85: C YDEWP(MN): THE AVERAGE DEWP FOR A MONTH FROM OUR INPUT SET.

86: C DEWMN(N,MN): THE MONTHLY AVERAGE DEWP FOR THE NTH INPUT YEAR.

87: C SLDEWP: THE AVERAGE DEWP FOR OUR SELECTED YEAR, DONE FOR EACH MONTH.

88: C DAWBTP(DAY): THE STORED WBTP VALUE FOR EACH DAY.

89: C WBTPSL( YEAR, TIN, LENGTH) : THE AVERAGE WBTP FOR OUR SELECTED SEGMENTS.

90: C WBTPSP( SEGMENT) : THE AVERAGE WBTP FOR THE SEGMENTS WE ARE CHOOSING

91: C FROM FOR A MONTH.

42: C WBTPX: A SUMMING VARIABLE TO HELP FIND THE AVERAGES FOR THE SEGMENTS.

93: C YWBTP(MN): THE AVERAGE WBTP FOR A MONTH FROM OUR INPUT SET.

94: C WBTPMN( N . MN) : THE MONTHLY AVERAGE WBTP FOR THE NTH INPUT YEAR.

95: C SLWBTP: THE AVERAGE WBTP FOR OUR SELECTED YEAR, DONE FOR EACH MONTH,

96: C DAWSP(DAY): THE STORED WNSP VALUE FOR EACH DAY.

97: C WNSPIN(MN): THE INPUT V/NSP VALUE TO BIAS OUR CHOICE OF SEGMENTS,

98: C IN METERS PER SEC. OR M.P.H.

99: C ZMWSDB: STORES A WIND SPEED-DRY BULB PRODUCT VALUE SO WE CAN WORK WITH

100: C IT MORE EASILY COMPUTED FROM WTJSPIN AND DBTP IN VALUES.

101: C WNSPSL( YEAR, MN, LENGTH) : THE AVERAGE WNSP FOR OUR SELECTED SEGMENTS.

102: C WNSPSP( SEGMENT) : THE AVERAGE mSP FOR THE SEGMENTS WE ARE CHOOSING

103: C FROM FOR A MONTH.

104: C WNSPX: A SUMMING VARIABLE TO HELP FIND THE AVERAGES FOR THE SEGMENTS.

105: C YWNSP(MN): THE AVERAGE WNSP FOR A MONTH FROM OUR INPUT SET.

106: C WNSPMN(N,MN) : THE MONTHLY AVERAGE WNSP FOR THE NTH INPUT YEAR.

107: C SLWSP: THE AVERAGE WNSP FOR OUR SELECTED YEAR, DONE FOR EACH MONTH.

108: C DACCAD(DAY): THE STORED CCAD VALUE FOR EACH DAY.

109: C CCADIN(MN): THE INPUT CCAD VALUE TO BIAS OUR CHOICE OF SEGMENTS,

110: C IN TENTHS.

Ill: C ZMCCT: STORES A CCTIN OR CCADIN VALUE SO WE CAN WORK WITH IT MORE EASILY.

112: C CCADSL( YEAR, MN, LENGTH) : THE AVERAGE CCAD FOR OUR SELECTED SEGMENTS.

A4
113: C CCADSP(SEGMEWT) : THE AVERAGE CCAD FOR THE SEGMENTS ARE CHOOSIRG

114: C FROM FOR A MONTH.

115: C CCADX: A SUMMING VARIABLE TO HELP FIND THE AVERAGES FOR THE SEGMENTS.

116: C YCCAD(MN): THE AVERAGE CCAD FOR A MONTH FROM OUR INPUT SET.

117: C CCAD?IN(N,MN) : THE MONTHLY AVERAGE CCAD FOR THE NTH INPUT YEAR.

118: C SLCCAD: THE AVERAGE CCAD FOR OUR SELECTED YEAR, DONE FOR EACH MONTH.

119: C DACCT(DAY): THE STORED CCT VALUE FOR EACH DAY.

120: C CCTIN(MN): THE INPUT CCT VALUE TO BIAS OUR CHOICE OF SEGMENTS,

121: C IN TENTHS.

122: C CCTSL( YEAR, MN, LENGTH) : THE AVERAGE CCT FOR OUR SELECTED SEGMENTS.

123: C CCTSP( SEGMENT) : THE AVERAGE CCT FOR THE SEGMENTS WE ARE CHOOSING

124: C FROM FOR A MONTH.

125: C CCTX: A SUMMING VARIABLE TO HELP FIND THE AVTIRAGES FOR THE SEGMENTS.

126: C YCCT(MN): THE AVERAGE CCT FOR A MONTH FROM OUR INPUT SET.

127: C CCTMN(N,MN): THE MONTHLY AVERAGE CCT FOR THE NTH INPUT YEAR.

128: C SLCCT: THE AVERAGE CCT FOR OUR SELECTED YEAR, DONE FOR EACH MONTH.

129: C DADRNG(DAY): THE STORED DRNG VALUE FOR EACH DAY.

130: C DRNGSL( YEAR, MN, LENGTH) : THE AVERAGE DRNG FOR OUR SELECTED SEGMENTS.

131: C DRNGSP( SEGMENT) : THE AVERAGE DRNG FOR THE SEGMENTS WE ARE CHOOSING

132: C FROM FOR A MONTH.

133: C DRNGX: A SUMMING VARIABLE TO HELP FIND THE AVERAGES FOR THE SEGMENTS.

134: C YDRNG(MN): THE AVERAGE DRNG FOR A MONTH FROM OUR INPUT SET.

135: C DRNGMN(N,MN) : THE MONTHLY AVERAGE DRNG FOR THE NTH INPUT YEAR.

136: C SLDRNG: THE AVERAGE DRNG FOR OUR SELECTED YEAR, DONE FOR EACH MONTH.

137: C DADDHT(DAY): THE STORED DDHT VALUE FOR EACH DAY.

138: C DDHTSL( YEAR, MN, LENGTH) : THE TOTAL DDHT FOR OUR SELECTED SEGMENTS.

139: C DDHTSP( SEGMENT) : THE TOTAL DDHT FOR THE SEGMENTS WE ARE CHOOSING

140: C FROM FOR A MONTH.

141: C DDHTX: A SUMMING VARIABLE TO HELP FIND THE TOTALS FOR THE SEGMENTS.

142: C YDDHT(MN): THE TOTAL DDHT FOR A MONTH FROM OUR INPUT SET.

143: C DDHTMN(N,MN) : THE MONTHLY TOTAL DDHT FOR THE NTH INPUT YEAR.

144: C SLDDHT: THE TOTAL DDHT FOR OUR SELECTED YEAR, DONE FOR EACH MONTH.

145: C DADDCL(DAY): THE STORED DDCL VALUE FOR EACH DAY.

146: C DDCLSL( YEAR, MN, LENGTH) : THE TOTAL DDCL FOR OUR SELECTED SEGMENTS.

147: C DDCLSP( SEGMENT) : THE TOTAL DDCL FOR THE SEGMENTS WE ARE CHOOSING

148: C FROM FOR A MONTH.

149: C DDCLX: A SUMMING VARIABLE TO HELP FIND THE TOTALS FOR THE SEGMENTS.

150: C YDDCL(MN): THE TOTAL DDCL FOR A MONTH FROM OUR INPUT SET.

151: C DDCLMN(N,MN) : THE MONTHLY TOTAL DDCL FOR THE NTH INPUT YEAR.

152: C SLDDCL: THE TOTAL DDCL FOR OUR SELECTED YEAR, DONE FOR EACH MONTH.

153: C DDBASH: THE BASE TEMPERATURE FOR THE DEGREE DAYS HEATING (DDHT), DEG. C.

154: C DDBASC: THE BASE TEMPERATURE FOR THE DEGREE DAYS COOLING (DDCL), DEG. C. 153: C BAPRIN(MN): INPUT MONTHLY BAROMETRIC PRESSURE KPASCAL, OR IN OF HG.

156: C DEWPIN(MN): INPUT MONTHLY DEWPOINT TEMPERATURE DEG. C. OR F. 157: C PVS: PARTIAL VAPOR PRESSURE CALCULATED FOR SEGMENT DEWPSP( I)

.

158: C ARRAY(216>: USED TO READ IN THE REAL VALUES FROM OUR WEATHER TAPES.

159: C ICDT(MN, 1-3) : THE STARTING DAYS OF OUR CHOSEN SEGMENTS AS THEY ARE IN

160: C THE DEFINE FILE.

161: C ICHYR(MN, 1-3) : THE YEAR OF OUR CHOSEN SEGMENTS (IN TERMS OF WHEN

162: C IT WAS READ IN)

.

163: C ICHYRl, ICHYR2, ICHYR3: TEMPORARY STORING PLACE FOR ICHYR VALUES.

164: C LSEG(MN, 1-3) : THE LENGTH OF THE SEGMENTS IN SELECTED YEAR.

165: C RLSEG: REAL NUMBER VALUE FOR LSEG(MN,3).

166: C RAVG: USED TO FIND THE PROPER WEIGHTING OF SEGMENTS IN AVERAGING.

167: C RANKSP( SEGMENT) : THE VALUE OF THE REGRESSION EQUATION FOR A SEGMENT.

168: C RAHKCH( MONTH, TYPE) : THE RANK OF THE CHOSEN SEGMENTS.

169: C DLCCT: THE DIFFERENCE BETWEEN THE INPUT MONTHLY CCT OR CCAD

170: C (CCTIN(MN) NOW IN ZMCCT) AND THE AVERAGE CCT (CCTSP(I)).

A5
171: C DLDBTP( SEGMENT) : THE DIFFERENCE BETWEEN THE INPUT MONTHLY DBTP

172: C (DBTPIN(MN) NOW IN ZMDBTP) AND THE AVERAGE DBTP (DBTPSF(I)).

173: C DLWAMR: THE DIFFERENCE BETV/EEN THE COMPUTED INPUT MONTHLY WAMR IN

174: C ZMWAMR AND THE AVERAGE WAMRSP.

175: C DLWSDB: THE DIFFERENCE BETWEEN THE COMPUTED INPUT MONTHLY WSDB IN

176: C ZMWSDB AND THE AVERAGE WSDBSP . 177: C DBDVMN(MN): THE AVERAGE DEVIATION OF THE DRY BULB AVERAGES OF THE SEGMENT

178: C IN THE MONTHS IN OUR INPUT TAPE, TIMES 10, DEC. C. 179: C LEAPS: THE NUMBER OF LEAP YEARS IN THE INPUT WATHER TAPE.

180: C NEXTMN,NEXTYR: THE MONTH AND YEAR OF THE MONTH AFTER THE ONE FOR

181: C WHICH WE ARE FINDING THE SELECTED SEGMENT.

182: C INIDAY: THE FIRST DAY OF OUR SELECTION FOR A MONTH.

183: C NENDAY: THE LAST DAY OF OUR SELECTION RANGE.

184: C ISLDT( YEAR, MN. LENGTH TYPE): THE STARTING DAY OF OUR SELECTED SEGMENTS.

185: C ISEG: THE LENGTH OF THE SEGMENT WE ARE LOOKING FOR NOW.

186: C NUMRUN: THE NUMBER OF SELECTED YEAR TAPES TO BE MADE.

187: C LOGUNI: THE LOGICAL UNIT TO BE USED FOR THIS OPERATION.

188: C INTAPE: THE NUMBER OF WEATHER TAPES TO BE DRAWN ON FOR OUR

189: C SEGMENT SELECTION FILE.

190: C NSPAN: THE NUMBER OF SEGMENTS AVAILABLE FOR A MONTH.

191: C L1DAY,L2DAY: USED TO KEEP TRACK OF INIDAY'S NEXT INCREASE.

192: C IDV: USED TO SEE WHICH SEGMENT OF THE MONTH WE ARE DEALING WITH.

193: C IDVARR( IDV) : CONTAINS THE ORDER OF SEGMENT SELECTION.

194: C NEXTDV,LASTDV: THE POSITION OF THE SEGMENTS SURROUNDING THE ONES

195: C WE ARE PICKING./

196: C SIGNX: USES THE ADJOINING MONTHS DRY3ULB INPUT AVERAGES TO DECIDE THE

197: C PLACEMENT OF THE SEGMENTS.

198: C lYRARR(NYR): STORES THE A.D. YEAR OF OUR INPUT WEATHER YEARS.

199: C IDAY: DAY OF MONTH OF DATA FROM DAY.

200: C NDAY( YEAR, MN) : THE NUMBER OF DAYS IN A MONTH.

201: C NDAYT(MN): THE TOTAL NUMBER OF DAYS IN A MONTH SUMMED

202: C FOR ALL THE INPUT YEARS.

203: C NUMBYR: THE TOTAL NUMBER OF YEARS OF WEATHER TAPE INPUT.

204: C NUMDAY: THE TOTAL NUMBER OF DAYS OF INPUT WEATHER TAPE.

205: C NODAY: THE NUMBER OF DAYS TO BE TAKEN FROM AN INPUT FILE.

206: C NOYR: THE NUMBER OF YEARS TO BE USED FROM AN INPUT FILE.

207: C INITYR: THE FIRST YEAR TO BE USED FROM AH INPUT FILE.

208: C MN: MONTH.

209: C ISIFLG: THE FLAG FOR SIGNALING IF AVERAGES ARE TO BE ENTERED IN 210: C NON-SI UNITS.

211: C IBPFLG: THE FLAG FOR SIGNALING IF BAROMETRIC PRESSURE AVERAGES ARE TO BE

212: C ENTERED OR IF THE INPUT TAPES AVERAGES SHOULD BE USED.

213: C IDBFLG: 1 FOR MAXIMUM MINIMUM AVERAGE USE, 0 FOR 24-AVERAGE USE.

214: C IIDBDV, I2DBDV, I3DBDV: USED TO DETERMINE THE SEGMENTS USED IN THE

215: C DEVIATION CALCULATIONS.

216: C IDAWN: THE MONTHLY VALUES FOR SUNRISE.

217: C LENGTH: THE MONTHLY VALUES FOR LENGTH OF DAYLIGTH.

218: G

219: C DECLARE THE DIMENSIONS (ALL TYPES BY NAME.).

220: G

221: DIMENSION ICDT( 12,3) , ISLDT( 10, 12,3) ,LSEG( 12,3) ,WBTPSL( 10, 12,3)

,

222: * NDAY( 10, 12) . NDAYT( 12) .RANKSP(83) ,RANKSL( 10, 12,3) , IDVARR( 12,5)

,

223: * ISEGDN( 10) , WARRAY(216) ,DRNGSL( 10, 12,3) ,DVAJIN( 12) , YDSD( 12)

,

224: * DACCT(3660) ,CCTSL( 10, 12,3) ,CCTSP(83) ,CCTIN( 12,3) , YWBTP( 12)

,

225: * DACCAD(3660) ,CCADSL( 10, 12.3) ,CCADSP(83) , YCCAD( 12) ,BAPRSP(a3)

,

226: * DADBTP(3660) , DBTPSL( 10. 12,3) .DBTPIN( 12,3) ,DBTPSP(83)

,

227: * CCTMN( 10, 12) , YCCT( 12) ,DBTPMN( 10, 12) ,YDBTP( 12) , YDDHT( 12)

,

228: * DRNGMN( 10, 12) , YDRNG( 12) ,DRNGSP(a3) ,DBDVMN( 12) ,BAPRIN( 12)

,

A6
229: * RANKCH( 12,3) , ICY( 12,3) , I YRARR( 10) ,DABAPR( 3660) ,DADRWG( 3660)

,

230: * DADDHT(3660) ,DDHTSL( 10, 12,3) ,DDHTSP(83) , DDHTMN( 10, 12)

,

231: * DADDCL(3660) ,DDCLSL( 10, 12,3) ,DDCLSP(a3) ,DDCLNN( 10, 12)

,

232: * CCTRW( 12) ,BAPRMN( 10, 12) , YDDCL( 12) ,DBTPRN( 13) ,SLDBTP( 12)

,

233: * DAWNSP(3660) , DAWBTP( 3660) , DADEWP( 3660) ,DEWPIN( 12,3) .WNSPINC 12.3)

,

234: * WNSPRK( 12) ,DEWPRN( 12) ,DEWSP(a3) , WNSPSP(83) , YBAPR( 12) . WBTPSP(a3) , 235: * WWSPSL( 10, 12,3) , DEWPSL( 10, 12,3) , WNSPNN( 10, 12) , DEWPMN( 10, 12)

,

236: * YWNSP( 12) ,BAPRSL( 10, 12,3) , YDEWP( 12) ,WBTPMN( 10, 12) ,CCADMN( 10, 12)

237: COMMON /DT/ IDAWN( 12) , LENGTH( 12)

238: C INITIALIZE VALUES.

239: DATA LSEG /10, 9 , 10 , 10 , 10, 10 , 10, 10, 10, 10 , 10, 10 , 1 1 , 10, 1 1 , 10 , 1 1 , 10, 1

1

240: * ,11,10,11,10,11,10,9,10,10.10,10,10,10,10,10,10,10/

241: DEFINE FILE 10 ( 3660 , 220 , V, NPLACE)

242: CALL PGSIZE (3,64)

243: C ENTER THE LATITUDE, LONGITUDE AND TIME ZONE FOLLO>fED

244: C BY THE NUMBER OF TAPES USED TO INPUT WEATHER YEARS,

245: C THE NUMBER OF SELECTED YEARS TO BE MADE, THE BASE

246: C TEMPERATURES FOR DEGREE DAYS CALCULATIONS AND THE

247: C HEIGHT TO BE USED FOR THE INPUT STATION.

248: PRINT 600

249: READ (5,500) RLATD, RLONG, TZN, INTAPE, NUMRUN, HI , IDBFLG, DDBASH, DDBASC

250: CALL SUNNY ( RLATD, RLONG, TZN)

25 1 : NUMRUN= NUMRUN+ 1

0

252: INTAPE= INTAPE+14

253: NUMBYR=0

254: NUMDAY=0

255: IDAY=0

256: C FOR EACH INPUT TAPE: READ THE PROPER YEARS INTO

257: C OUR DEFINE FILE AND INCREMENT OUR YEAR AND DAY

258: C COUNTS AS NEEDED. THE FIRST YEAR OF THE TAPE TO

259: C BE USED AND THE NUMBER OF YEARS TO BE USED MUST

260: C BE INPUT AS REQUESTED.

261: DO 50 LOGUNI= 15 , INTAPE

262: PRINT 605 LOGUNI

263: READ (5,500) INITYR.NOYR

264: C SEE IF WE ARE AT THE START OF THE PROPER YEAR, IF NOT

265: C READ UNTIL WE GET THERE.

266: READ (LOGUNI) WARRAY, lYEAR, MN, ID, ICITY

267: REWIND LOGUNI

268: IF ( lYEAR-INITYR) 10,20,20

269: 10 READ (LOGUNI) WARRAY, I YEAR, MN, ID, ICITY

270: IF ( IYEAR-INITYR+MN+ID-42) 10,,

271: C FIND THE INCREMENT FOR OUR DAY AND YEAR COUNTS AND USE THEM

272: 20 LEAPS=(MOD( INITYR-l,4)+N0YR)/4

273: N0DAY=365*N0YR+LEAPS

274: DO 30 1= l.NOYR

275: IYRARR(NWIBYR+I) = INITYR+I-1

276: 30 CONTINUE

277: INITYR= INITYR-NUMBYR

278: C FOR EACH DAY OF WEATHER DATA CALL DAYV TO

279: C GET THE DAILY AVERAGES AND OTHER NECESSARY

280: C INFORMATION AND STORE IT IN THE DA ARRAYS

281: C ALSO SUM THE VALUES IN OUR MONTHLY AVERAGE ARRAYS.

282: C NOTE: DAYW WRITES INTO THE DEFINE FILE.

283: DO 40 K=1,N0DAY

284: CALL DAYW ( MN, ID, DBTP , DRNG, CCT, CCAD, DDHT, DDCL, DEWP, WBTP,

285: * WNSP, IYEAR,NUMDAY, INITYR.LOGUNI , DDBASH, DDBASC, BAPR, IDBFLG>

286 : DACCT( NUNDAY) = CCT

A7
287 : DACCAD( NtJMDAY) = CCAD

288: CCTMN( I YEAR. MN) =CCTMN( lYEAR, MN) +CCT

289 : CCADMN( I YEAR, MN) =CCADMR( I YEAR, MN) +CCAD

290 : DADBTP( NUMDAY) = DBTP

291: DBTPMN( lYEAR, MN) =DBTPMN( lYEAR, MN) +DBTP

292 : DADRI^G( NUMDAY) = DRKG

293: DRNGMN( IYEAR,MN)=DRNGMN( IYEAR,MN)+DRNG

294: DADEWP( NUMDAY) =DEWP

295: DAWBTP( NUMDAY) =VBTP

296: DEWPMN( I YEAR, MN) =DEWMN( I YEAR, MN) +DEWP

297: DAWNSP( NUMDAY) = WISP

298: WKSPMN( I YEAR, MN) = VNSPMN( I YEAR, MN) +WNSP

299: DADDHT( NUMDAY) =DDHT

300: DDHTMN( IYEAR,MN)=DDHTMN( IYEAR,MN)+DDHT

30 1 : DADDCL( NUMDAY) = DDCL

302: DDCLMN( I YEAR, MN) =DDCLMH( I YEAR, MN) +DDCL

303: DABAPR( NUMDAY) =BAPR

304: BAPRMN( IYEAR,MN)=BAPRMN( IYEAR,MN)+BAPR

305: VBTPMN( IYEAR,MN)=WBTPMN( IYEAR,MN)+WBTP

306: NDAY( IYEAR,MN)=ID

307: 40 CONTINUE

303: NTJMBYR=NUMBYR+NOYR

309: 50 CONTINUE

310: PRINT 610

311: C FOR EACH MONTH: GET THE FULL SET MONTHLY AVERAGES

312: C AND ^VRITE THEM OUT.

313: DO 70 MN= 1 , 12

314: C FOR EACH YEAR: SUM THE MONTHLY TOTALS, GET THE MONTHLY

315: C AVERAGES AND VRITE THEM OUT.

316: DO 60 NYR=1,NUMBYR

317: N=NDAY(NYR.MN)

318: YCCT(MN)=YCCT(MN)+CCTMN(NYR,MN)

319: YGCAD(MN)=YCCAD(MN)+CCADMN(NYR,MN)

320: CGTMN(NYR,MN)=CCTMN(NYR,MN)/N

321: CCADMN(NYR,MN)=CCADMN(NYR,MN)/N

322: YDBTP(MN)=YDBTP(MN)+DBTPMN(NYR,MN)

323: DBTPMN(NYR,MN)=DBTPMN(NYR,MN)/N

324: YDRNG(MN)=YDRNG(MN)+DimGMN(NYR,MN)

325: DRWGMN(NYR,MN)=DRNGMN(NYR,MN)/N

326: YDEW(MN)=YDEWP(MN)+DEVPMN(NYR,MN)

327: YWBTP(MN)=YWBTP(fIN)+WBTPMN(NYR,MN)

328 : DEWMN( NYR, MN) = DEWPMN( NYR, MN) /N

329: WBTPMN(NYR,MN)=WBTPMN(NYR,MN)/N

330: Y^VWSP(MN) =YraSP(MN)+WSPMN(NYR,MN)

331: ¥NSPMN(NYR,MN)=WNSPMN(NYR,MN)/N

332: YBAPR(MN) =YBAPR(MN)+BAPRMN(NYR,MN)

333: BAPRMN(NYR,MN)=BAPRMN(NYR,MN)/N

334: YDDnT(MN)=YDDHT(MN)+DDHTMN(NYR,MN)

335 : YDDCL( MN) = YDDCL( MN) +DDCLMN( NYR, MN)

336 : NDAYT( MN) = NDAYT( MN) +N

337: PRINT 615 I YRARR( NYR) , MN, DBTPMN( NYR, MN) , DRNGMN( NYR, MN)

,

338: * DEWMN(NYR,MN) ,WBTPMN(NYR,MN) ,CCTMN(NYR,MN) ,CCADMN(NYR,MN)

,

339 : * WWSPMN( NYR, MN) , BAPRMN( NYR, MN) , DDHTMN( NYR, MN) , DDCLMN( NYR, MN)

340: 60 CONTINUE

341: IDVARR(MN, 1)=2

342: N=NDAYT(MN)

343: YCCT(MN)=YCCT(MN)/N

344: YCCAD(MN)=YCCAD(MN)/N

A8
345: YDBTP( MN) = YDBTP( UN) /If 346: YDRNG( MN) = YDRRG( MN) /R

347: YDEWP( MN) = YDEWP( MN) /N

348: YWBTP ( MN ) = Y>mTP ( MN) /N

349: YVWSP( MN) = YVNSP( MN) /N

350: YBAPR( MN) = YBAPR( MN) /N

351: YDDCL( MN) = YDDCL( MN) /NUMBYR

352: YDDHT( MN) = YDDHT( MN) /NUMBYR

353: PRINT 620 MN, YDBTP(MN) , YDRNG(MN) ,YDEW(MN) ,YWBTP(MN) , 354: * YCCT( MN) , YCCAD( MN) . YVNSP( MN) , YBAPR( MN) , YDDHT( MN) , YDDCL( MN)

355: 70 CONTINUE

356: C FOR EACH YEAR:

357: DO 90 NYR=1, NUMBYR

358: C FOR EACH MONTH:

359: DO 90 MN= 1, 12 360: G FIND THE DAILY STANDARD DEVIATION.

361: N=NDAY( NYR.MN)

362- DSDM=0.0

363: DO 80 ND=1,N

364- IDAY= IDAY+1

365 DSDM=DSDM+(DBTPMN(NYR,MH)-DADBTP( IDAY) )**2

366 80 CONTINUE

367 DSDM= SQRT( DSDM/ ( N- 1 )

)

368 YDSDC MN) = YDSD( MN) +DSDri/NUMBYR

369 90 CONTINUE

370 G FOR EACH SELECTED YEAR TO BE CREATED:

371 DO 400 L0GUNI=11,NUMRUN

372 G FIND HOW THE AVERAGES ARE TO BE ENTERED AND READ

373 G THEM IN FOR EACH MONTH. IF CALCULATIONS ARE NEEDED,

374 G MAKE THEM.

375 PRINT 625 LOGUNI

376 READ (5,500) ISIFLG, IBPFLG,H2, IVBFLG, ICCFLG

377 DELH=H2-H1

378 PRINT 630

379 IF ( ISIFLG. EO. 1) PRINT 635

880 IF ( ISIFLG. EO.O) PRINT 640

381 DO 1 10 MN= 1, 12 382 PRINT 645 MN

383 READ (5,500) DBTPRN(MN) ,CCTRN(MN) , WSPRN(MN) , 384 * DEVPRN(MN) ,BAPRIN(MN) ,DVAJiN(MN)

385 IF ( ISIFLG. EQ.O) GO TO 100

386 DBTPRN( MN) = ( DBTPRN( MN) -32 . 0) *5/9

387 DEWPRN( MN) = ( DEVPRN( MN) -32 .0) *5/9

388 WSPRN( MN) = WNSPRN( MN) * 1 . 609/3 . 6

389 BAPRIN(MN)=BAPRIN(MN)*101. 1/29.921

390 100 IF ( IBPFLG.EQ. 1) BAPRIN( MN) = YBAPR( MN) /10. **( DELH/( ( ( DBTPRN( MN)

391 * +YDBTP( MN) )/2. 0+273. ) *67. 4072)

)

392 IF ( IVBFLG. EQ. 1) CALL DEWNT( DBTPRN( MR) , DEWPRN( MN) , BAPRIN( MN) , 393 * DEVPRN( MN)

)

394 : 110 CONTINUE

395 LASTMN= 12 396 DBTPRN( 13) =DBTPRN( 1) 397 : G FOR EACH MONTH:

398 DO 130 MN= 1, 12 399 : C DECIDE THE ORDER OF SEGMENT SELECTION USIRG DRY BULB

400 : C DIFFERENCE.

401 REXTMN=MN+1

402 IDVARR(MN,2)=1

A9
403: IDVARRCWN,3)=3

404: IDVARR(MN,4)=

1

405: IDVAIIR(MH,5)=3

406: IF (ABS(DBTPRN(MN)-DBTPRN(LASTMN)) .LT.ABS(DBTPRW(MIT)

407: * -DBTPRN( NEXTfIN) ) ) GO TO 120

408: IDVAIIR(MN,2)=3

409: IDVARR(MN.3)=1

410: IDVARIl(MN,4)=3

411: IDVARR(MN,5) = 1 412: 120 DBTPIN(MN,2)=DBTPRIl(MN)

413: DEVPIN(MN,2)=DEWPRN(MN)

414: CCTIN(MN,2)=CCTRN(MN)

415: WSPIN(MN,2)=WNSPRH(MR)

416: LASTMN=MN

417: 130 CONTINUE

418: C CHOOSE SEGMENTS FIVE TIMES:

419: C FIRST BASED SOLELY ON THE INPUT AVERAGES.

420: C SECOND BASED SOLELY ON THE DRY BULB AVERAGE + OR - 421: C THE AVERAGE SEGMENT DEVIATION TIMES 10. 422: C THIRD BASED ON THE AVERAGES TO BALANCE PICKS 1 AND 2.

423: C FOURTH BASED ON THE AVERAGES TO BALANCE PICKS 1 AND 3.

424: C FIFTH BASED ON THE AVERAGES TO BALANCE PICKS 1 AND 4. 425: DO 240 IDV=1,5

426: C INITIALIZE THE STARTING AND ENDING DAYS FOR OUR PICIffi. 427: L1DAY=0

428: L2DAY=0

429: INIDAY=0

430: NENDAY=31

431: C FOR EACH YEAR:

432: DO 170 r{YR= 1 , NOTIBYR

433: C FOR EACH MONTH:

434: DO 170 MN= 1 , 12

435: C FIND THE STARTING AND ENDING DAYS OF OUR AVAILABLE DATA.

436: C FIND THE STARTING AND ENDING DAYS OF OUR SEGMENTS TO BE

437 f C USED IN THE TEN DAY DEVIATION CALCULATIONS.

438 : 1 1 DBDV= L2DAY-4

439: I2DBDV= I lDBDV+NDAY(NYR,MN)-6

440: IF (MN.EQ. 1. AND.NYR.EQ. 1) I 1DBDV= I lDBDV+4

441: NEXTYR=NYR+MN/12

442: NEXTMN=MOD(MN, 12)+1

443: IF ( NUMBYR-NEXTYR) 140,,

444 : NENDAY= NENDAY+NDAY( NEXTYR, NEXTMN)

445 : I 2DBDV= I 2DBDV+6

446: 140 INIDAY= INIDAY+LIDAY

447: I3DBDV=(( I2DBDV- I lDBDV+2) /2) *( ( I2DBDV- I 1DBDV+ 1) /2)

448 : NDV= I DVARR( MN , I DV)

449: ISEG=LSEG(MN,NDV)

450: C MOVE THE MONTHLY AVERAGES TO BE USED IN THIS SELECTION

451: C TO THE ZM VARIABLES.

452: ZMDBTP=DBTPIN(MN,NDV)

453: ZNCCT=CCTIN(MN,NDV)

454: ZNWAMR=62.2*PVSF(DEWIN(MN,NDV) )/(YBAPR(MN)-

455: * PVSF(DEVPIN(MN,NDV)))

456 : ZMVSDB= WNSP I N( MN , NDV) * ( 55 . 0/3 . 0-DBTP IN( MN , NDV)

)

457: NSPAN=NENDAY-INIDAY-ISEG+1

458: ITEMP=1

459: CCTX=0.0

460: CCADX=0.0

AlO
461: DBTPX=0.0

462: DRNGX=e.e

463: DEWPX=0.0

464: WBTPX=0.0

465: ¥tfSPX=0.0

466: DDHTX=0.0

467: DDCLX=0.0

468: BAPRX=0.0

469: C FOR EACH DAY OF TEE FIRST SEGMENT ADD ITS VALUES

470: C TO THE SEGMENT AVERAGING VARIABLES.

471: DO 150 J= 1, ISEG

472: K=J+INIDAY

473 : CCTX= CCTX+DACCT( K)

474 : CCADX= CCADX+DACCAD( K)

475 : DBTPX= DBTPX+DADBTP( K)

476: DRNGX=DRNGX+DADRNG(K)

477: DEWPX=DEWX+DADEWP(K)

478: WBTPX=VBTPX+DAWBTP(K)

479: Wr}SPX=WWSPX+DAWNSP(K)

480 : DDHTX= DDHTX+DADDHT( K)

481: DDCLX=DDCLX+DADDCL(K)

482: BAPRX=BAPRX+DABAPR(K)

483: 150 CONTINUE

484: C FOR EACH SEGMENT AVAILABLE TO THIS MONTH:

485: DO 160 I=1,NSPAN

486: C FOR ALL THE SEGMENTS EXCEPT THE FIRST:

487: IF (I.Ea. 1) GO TO 155

488: LP=K+I-1

489: C ADJUST THE SUMMING VARIABLES BY ADDING THE WEXT

490: C DAY AND SUBTRACTING THE FIRST ONE.

491: LM=LP-ISEG

492 : CCADX=CCADX+DACCAD( LP) -DACCAD( LM)

493: CCTX=CCTX+DACCT(LP)-DACCT(LM)

494: DRNGX=DRriGX+DADRNG(LP)-DADRNG(LM)

495: DBTPX=DBTPX+DADBTP(LP)-DADBTP(LM)

496: DEWPX=DEWPX+DADEVP(LP)-DADEW(LM)

497: VBTPX=VBTPX+DAWBTP(LP)-DAWBTP(LM)

498: WNSPX= VNSPX+DAWNSP( LP) -DAVNSP( LM)

499: BAPRX=BAPRX+DABAPR(LP)-DABAPR(LM)

500 : DDHTX= DDHTX+DADDHT( LP) -DADDHT( LM)

50 1 : DDCLX= DDCLX+DADDCL( LP) -DADDCL( LM)

502: C DIVIDE BY THE SEGMENT LENGTH TO GET THE AVERAGES.

503: 155 DBTPSP( I)=DBTPX/ISEG

504: DRNGSP( I)=DRNGX/ISEG

505: DEWPSP( I)=DEWPX/ISEG

506: WBTPSP( I)=WB1TX/ISEG

507: VNSPSP( I)=VNSPX/ISEG

508: CCTSPC I)=CCTX/ISEG

509: CCADSP( I)=CCADX/ISEG

510: BAPRSP( I)=BAPRX/ISEG

511: DDHTSP( I)=DDHTX

512: DDGLSP( I)=DDCLX

513: PVSFSP=PVSF(DEWPSP( I))

514: WAMRSP=62.2*PVSFSP/(BAPRSP( I)-PVSFSP)

515: WSDBSP=WNSPSP( I)*(55.0/3.0-DBTPSP( I)

)

516: DLCCT=CCTSP( I)-ZMCCT

517: IF ( ICCFLG.Ea. 1) DLCCT=CCADSP( I)-ZMCCT

518: DLDBTP=DBTPSP( I)-ZMDBTP

All
519: DLVAMR= WAMRSP-ZMWAMR

520: DLVSDB=VSDBSP-ZMWSDB

521: C FIRD THE RANK AND SEE IF IT IS THE BEST FOR THIS

522: C TYPE AND MONTH.

523: RANKSP( I) = 14 . 299*ABS( DLDBTP) +4.90*ABS( DLCCT)

524: * .37*ABS(DLWSDB)+96.6*ABS(DLWAMR)

525: IF (IDV.EQ.2) RANKSP( I

) =ABS( DLDBTP)

528: IF ( IDV.EQ. l.AND.LOGUNI.EQ. 11. AND. I.GE. IIDBDV.AND.

527: * I.LE.I2DBDV) DBDVMN( MN) = ABS( DBTPSP( I

)

528: * DBTPMN(NYR,MN) )*10.O/I3DBDV/NUMBYR+DBDVMN(MN)

529: IF (RANKSP( I) .LT.RANKSP( ITEMP) ) I TEMP=

I

530: 160 CONTINUE

531: C STORE TEE WEATHER AVERAGES OF THE BEST SEGMENT IN THIS

532: C MONTH FOR THIS TYPE ( NDV) IN THE SL ARRAYS.

533: CCTSL( NYR.MN, NDV) =CCTSP( ITEMP)

534: CCADSL( NYR.MN. NDV) =CCADSP( ITEMP)

535: DBTPSL( NYR.NN, NDV) =DBTPSP( ITEMP)

536: BAPRSL( NYR.MN. NDV) =BAPRSP( ITEMP)

537: DRNGSL( NYR.MN, NDV) =DRNGSP( ITEMP)

538: DEWPSL( NYR.MN, NDV) =DEWPSP( ITEMP)

539: VBTPSL( NYR.MN, NDV) =WBTPSP( ITEMP)

540: WNSPSL( NYR.MN, NDV) =WNSPSP( ITEMP)

541: DDHTSL( NYR.MN, NDV) =DDHTSP( ITEMP)

542: DDCLSL( NYR.MN, NDV) =DDCLSP( ITEMP)

543: ISLDT(NYR,MN,NDV)= ITEMP+INIDAY

544: IIANKSL( NYR,MN, NDV) =RANKSP( ITEMP)

545: L1DAY=L2DAY

546 : L2DAY= NDAY( NYR, MN)

547: 170 CONTINUE

548: C FOR EACH MONTH.

549: LASTMN=12

550: DO 220 MN= 1, 12

551: IF (IDV.NE.l) GO TO 180

552: C ADJUST THE DRY BULB TO PREPARE FOR THE SECOND

553: C SELECTION USING THE TEN DAY AVERAGE DEVIATION.

554: NEXTMN=MN+1

555: SIGNX=DBTPRN(LASTMN)-DBTPRN(NEXTMN)

556: LASTMN=MN

557: SIGNX=SIGNX/ABS(SIGNX)*1.0

558: DBTPIN(MN, 1)=DBTPRN(MN)+SIGNX*DBDVMN(MN)*DVAJIN(MN)

559 : DBIT IN( MN. 3) = DBTPRN( MN) -S IGNX*DBDVMN( MN) *DVAJ IN( MN)

560: 180 NDV= IDVARR(MN. IDV)

561: DO 190 I=1,NUMBYR

562: RANKSP( I)=RANRSL( I.MN.NDV)

563: 190 CONTINUE

564: C INITIALIZE ISEGDN.

565: DO 200 1=1, 10

566: ISEGDN(I)=I

567: 200 CONTINUE

568: C CALL ORDER AND PLACE THE CHOSEN SEGMENT VALUES INTO THE CH

569: C ARRAYS AVOIDING EXCESSIVE OVERLAP IN THE CHOSEN DAYS.

570: CALL ORDER (RANKSP, ISEGDN, NUMBYR)

571: ITENP= ISEGDN( 1) 572: ICDT(MN,NDV) = ISLDT( ITEMP , MN, NDV)

573: ICY( MN, NDV) = ITEMP

574: RANKCH( MN. NDV) =RANKSL( ITEMP. MN, NDV)

575: ITEMP=2

576: IF (IDV.LT.4) GO TO 220

A12
577: IF (ABS( ICDT(MN,NDV)-ICDT(MN,2)) .GT.5) GO TO 210

578: ITEMP= ISEGDH(2)

579 : I CDT( MN , NDV) = I SLDT( I TEMP

.

m

,

WDV)

580: fCY(MN,NDV) = ITEMP

581: RAWKCH(riN,NDV)=RANIffiL( ITENP , MN, NDV)

582: ITEMP=3

583: 210 IF (IDV.LT.5) CO TO 220

584 : LASTDV= I DVARR( MN , I DV- 1

)

585: IF (ABS( ICDT(MN,NDV)-ICDT(MN,LASTDV)) .GT.5) GO TO 220

586: 1TEMP= ISEGDN( ITEMP)

587: ICDT(MN,NDV)= ISLDT( ITEMP, MN. NDV)

588: ICY(MN. NDV) = ITEMP

589 : RANKCH( MN , NDV) = IIANKSL( I TEMP , MN . NDV)

590: IF (ABS( ICDT(MN,NDV)-ICDT(MN,2)) .GT.5) GO TO 220

591: ITEMP= ISEGDN(3)

592: ICDT(MN,NDV)= ISLDT( ITEMP, MN, NDV)

593: ICY( MN, NDV) = ITEMP

594: RAJ>IKGH( MN, NDV) =RANKSL( ITEMP, MN, NDV)

595: 220 CONTINUE

596: IF ( IDV.LT.2.0R. IDV.EQ.5) GO TO 240

597: C ADJUST THE AVERAGES WE ARE USING TO SELECT THE NEXT

598: C SEGMENT, TO BALANCE THE ERRORS IN AVERAGES

599: C OF THE SEGMENTS VE HAVE ALREADY FOUND.

600: DO 230 1=1, 12 601: NDV= IDVARR( I, IDV)

602 : NEXTDV= IDVARR( I . IDV+ 1

)

603: ICY1= ICY( I.NDV)

604: ICY2=IGY( 1,2)

605: RLSEG=LSEG( I ,3)

606 : RAVG= LSEG( 1,2) /RLSEG+2 .

0

607: DBTPIN( I , NEXTDV) =RAVG*DBTP IN( I,2)-DBTPSL( ICY2, 1,2)*

608: * (IlAVG-2.0)-DBTPSL( ICYl, I,NDV)

609 : CCTIN( I , NEXTDV) =RAVG*CCTIN( 1 , 2) -CCADSL( ICY2 ,1.2)*

610: * (RAVG-2.0)-CCADSL( ICYl, I.NDV)

611: IF ( ICCFLG.EQ.0) CCTIN( I , NEXTDV) =RAVG*CCTIN( 1 , 2)

612: * -CCTSL( ICY2, 1,2) *(RAVG-2.0)-CCTSL( ICYl, I.NDV)

613: VNSPINC I . NEXTDV) =RAVG*WISP IN( I,2)-WNSPSL( ICY2, 1,2)*

614: * (RAVG-2.0)-WNSPSL( ICYl, I.NDV)

615: DEWPIN( I , NEXTDV) =RAVG*DEWP IN( I,2)-DEWPSL( ICY2, 1,2)*

616: * (RAVG-2.0)-DEWPSL( ICYl, I.NDV)

617: 230 CONTINUE

618: 240 CONTINUE

619: ITEMP=NDAY( 1,2)

620: NDAY{1,2)=28

621: PRINT 650

622: C FOR EACH MONTH GET THE SELECTED YEAR AVERAGES,

623: DO 250 1= 1, 12 624: N=NDAY(1,I)

625: L1 = LSEG(I,1)

626: L2=LSEG(I.2)

627: L3=LSEG(I,3)

628: ICY1= ICY( I, 1) 629: ICY2= ICY( 1 ,2)

630: ICY3=ICY( 1,3)

631: SLCCT= ( CCTSL( ICYl, 1, 1)*L1+CCTSL( ICY2, 1,2)

632: * *L2+CCTSL( ICY3, I,3)*L3)/N

633: SLCCAD= ( CCADSL( ICYl, I, 1 ) *L1+CCADSL( ICY2. 1,2)

634: * *L2+CCADSL( ICY3, I,3)*L3)/N

A13
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637

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684 610

685 * 686 * 687 * 688 * 689 * 690 * 691 615

692 *

SLBAPR= ( BAPRSL( ICYl , I, l)*Ll+BAPRSL( ICY2, 1,2)

*L2+BAPRSL( ICY3 ,1,3) *L3) /N

SLDBTP( I) = (DBTPSL( ICYl, I. 1 ) *L1+DBTPSL( ICY2, 1,2)

*L2+DBTPSL( ICY3, I,3)*L3)/'N

SLDRNG= ( DRNGSL( ICYl, I, 1 ) *Ll+DimGSL( ICY2, 1,2)

*L2+DRNGSL( ICY3, I,3)*L3)/N

SLDEWP= ( DEWPSL( ICYl, I, 1 ) *L1+DEWPSL( ICY2, 1,2)

*L2+DEVPSL( ICY3, I,3)*L3)/'N

SLWBTP= ( WBTPSL( ICYl, I, 1 ) *L1+VBTPSL( ICY2, 1,2)

*L2+VBTPSL( ICY3, I,3)*L3)/N

SLWNSP= ( raSPSL( ICYl, I. 1 ) *L1+WWSPSL( ICY2, 1,2)

*L2+WISPSL( ICY3, I,3)*L3)/'N

SLDDHT=DDHTSL( ICYl, I, 1)+DDHTSL( ICY2, I, 2)+

DDHTSL( ICY3, 1,3)

SLDDCL=DDCLSL( ICYl, I, 1)+DDCLSL( ICY2, I, 2)+

DDCLSL( ICY3, 1,3)

AND PRINT THEM OUT.

PRINT 655 I,SLDBTP( I) ,SLDRNG,SLDEW,SLWBTP,SLCCT,SLCCAD,SLWWSP

,SLDDHT,SLDDCL

PRINT 660 DBTPSL( ICYl, I, 1) ,DEWSL( ICYl, I, 1) ,VBTPSL( ICYl, I, 1) , CCTSL( ICYl , 1 , 1 ) , CCADSL( ICYl , 1 , 1 ) , WNSPSL( ICYl , 1 , 1

)

PRINT 665 DBTPIN( I, 1) ,DEVPIN( I, 1) ,CCTIN(I, 1) ,WNSPIN( I, 1) PRINT 660 DBTPSL( ICY2, 1,2) ,DEWPSL( ICY2, 1,2) ,WBTPSL( ICY2, 1,2)

,

CCTSU IGY2, 1,2) ,CCADSL( ICY2, 1,2) , WSPSU ICY2, 1,2)

PRINT 665 DBTPIN( 1,2) ,DEWIN( 1,2) ,CCTIN( 1,2) . WSPIN( 1,2)

PRINT 660 DBTPSU ICY3, 1,3) ,DEWPSL( ICY3, 1,3) ,WBTPSL( ICY3, 1,3) , CCTSU ICY3, 1,3) ,CCADSL( ICY3, 1,3) ,VNSPSL( ICY3, 1,3)

PRINT 665 DBTPIN( 1,3) ,DEVPIN( 1,3) ,CCTIN( 1,3) ,VNSPIN( 1,3)

CONTINUE

NDAY( 1,2)=ITEMP

USING SLYEAR VRITE OUR SELECTED YEAR AT LOGUWI.

CALL SLYEAR ( DADBTP, SLDBTP. ICDT, LSEG, LOGUNI , lYRARR, RANKCH)

CONTINUE

PRINT 670 DBDVMN.YDSD

PRINT 680

' THE NUMBER OF WEATHER TAPES YOU ARE DRAWING FROM '

,

'1-10',/,' FOLLOWED BY THE NUMBER OF SELECTED YEARS YOU ARE

'MAKING 1-4',/,' FOLLOWED BY THE HEIGHT IN METERS OF THE',

' INPUT STATION',/,' FOLLOWED BY THE FLAG FOR DRY BULB AVERAGES

,/,' 1 IF THE AVERAGE OF THE MAXIMUM AND MINIMUM IS USED',

/,' 0 IF THE 24HR AVERAGE IS USED ',/,' FOLLOWED BY THE BASE ', 'TEMPERATURES FOR DEGREE DAY CALCULATIONS',/,' HEATING THEN ', 'COOLING BOTH IN DEGREES CELSIUS')

'ORMAT (

' ENTER THE FIRST YEAR YOU ARE USING FROM THE ',

' WEATHER TAPE ASSIGNED TO ',/ ,' LOGICAL UNIT', 13,' AND

'THE NUMBER OF CONSECUTIVE YEARS OF IT YOU ARE USING')

FORMAT (' YEAR OF MONTHLY AVG. MONTH DRY BULB DAILY',

' DEWPOINT WET BULB TOTAL DAYTIME WIND BAROMETRIC

' DEGREE DAYS' ,/,29X,

' TEMPERATURE RANGE TEMPERATURE TEMPERATURE CLOUD CLOUD '

,

•SPEED PRESSURE HEATING COOLING' ./, 3 IX, ' DEG. C. DEG. C. 'DEC. C. DEG. C. TENTHS TENTHS M./S. KILOPASCAL DEG.',

' C. * DAYS')

FORMAT ( 4X, ' AVG. OF ' , 14, 7X, I2,3X, 3( F9 . 2) , 7X, F5 . 2, F8. 2, F7.2,

A14
693: 620 FORMAT (/,' AVG. OF ALL YEARS' , 17, 3X. 3( F9 . 2) , 7X, F5 . 2, F8. 2 , F7. 2,

694: * F8.2.3X,3(F8.2) ,//)

695: 625 FORMAT (' ENTER THE AVERAGES YOU ARE USING TO SELECT THE',

696: * • SELECTED YEAR ' ,/, ' ASSIGNED TO LOGICAL UNIT' , 13,

697: */,' ENTER THE SI FLAG:',/,' 0 FOR SI UNITS (DEC. C. , M/SEC. , '

,

698: * ' KPASCAL) ' 699: * • 1 FOR ENGLISH UNITS (DEG. F. , IN. OF HG. , M.P.H.)',

700: * /,' FOLLOWED BY THE BP FLAG:',/,' IF YOU ARE UNABLE TO FIND ', 701: * 'BAROMETRIC PRESSURE AVERAGES',/,' ENTER 1 AND THE HEIGHT OF ', 702: * 'THE SELECTED LOCATION IN METERS',/,' ELSE ENTER 0 0.0',/,

703: * • FOLLOWED BY THE WB FLAG:',/,' IF YOU ARE ENTERING WET BULB ' 704: * 'INSTEAD OF DEWOINT ENTER 1',/,' ELSE ENTER 0',/,' FOLLOWED BY ' 705: * ,'THE CCT FLAG:',/,' 1 IF USING DAYTIME CLOUD AVERAGES INSTEAD ' 706 : * , ' OF FULL DAY AVERAGES ' , /

,

' ELSE ENTER 0 '

)

707: 630 FORMAT ( ' THE MONTH IS PRINTED FOR CONVENIENCE ONLY AND',

708: * ' SHOULD NOT BE INPUT'

,

709: * /,8X, ' DRY BULB MEAN SKY WIND DEW POINT?WET BULB',

710: * • BAROMETRIC DEVI AT ION' ,/, 8X, ' TEMPERATURE COVER SPEED',

711: * ' TEMPERATURE PRESSURE ADJUSTMENT')

712: 635 FORMAT (' MONTH DEG. F. TENTHS MI./HR. DEG. F. ', 713: * ' IN. OF HG. ') 714: 640 FORMAT (' MONTH DEG. C. TENTHS M./S. DEG. C. ', 715: * ' KILOPASCAL')

716: 645 FORMAT ( 14)

717: 650 FORMAT (/, ' AVERAGE OF THE SELECTED YEAR',

718: *///,3X, ' MONTH DRY BULB DAILY DEWPOINT ' 719: * ,' ^^T BULB TOTAL DAYTIME WIND

720: * ' DEGREES DAYS DEGREES DAYS' ,/, 9X, ' TEMPERATURE RANGE

72 1 : * ' TEMPERATURE TEMPERATURE CLOUD CLOUD '

,

722: * ' SPEED HEATING COOLING')

723: 655 FORMAT ( / , 16 , 9( 4X, F9 . 3)

)

724: 660 FORMAT (' CHOSEN' , F9 . 3, 17X, F9 . 3 , 2( 4X, F9 . 3) , 4X, F9 . 3. 4X, F9 . 3)

725: 665 FORMAT ( ' DES IRED' , F9 . 3 , 17X, F9 . 3, 24X, F9 . 3, 10X, F9 . 3)

726: 670 FORMAT (/, 37X, ' AVERAGE SEGMENT DEVIATION',/,' MONTH' , 6X, ' 1 ', 7X,

727: * '2' ,7X, '3' ,7X, '4' ,7X, '5' ,7X, '6' ,7X, '7' ,7X, ' 8' , 7X, ' 9

'

, 6X, ' 10' ,6X,

728: * ' 1

1

' ,6X, ' 12' ,/, ' DEG. C. ', 12( F8. 3) ,/, 4 IX, ' STANDARD DEVI ATION'

729: * ,/,' DEG. C. ' , 12(F8.3)

)

730: 630 FORMAT (' FOR MORE COMPLETE DESIGN AND TEMPERATURE',

731: * ' EXTREMES INFORMATION',/,' USE THE ADJUST PROGRAM IN',

732: * ' AVERAGE FINDING MODE.').

733: STOP

734: END

A15

APPENDIX B. Listings of the Main Program of the FORTRAN Program ADJUST.

This Program Requires the Following Subroutines and

Functions;

Subroutine CLOUD

Subroutine DEWPNT

Subroutine SUNNY

Subroutine WETBLB

Function DPF

Function PVSF

Function WBF

Listing in APPENDIX C.

Synopsis of FORTRAN Program ADJUST

Read in one year's decoded weather

data or, composite year from SELECT.

Compute and store monthly averages

and print out climatic summary for user.

Read input monthly climatic averages.

Calculate monthly and daily ratios

for adjuting hourly weather data.

Adjust the hourly values.

Write out the adjusted synthetic

weather year in decoded format.

Print out summary of the adjusted

synthetic weather year.

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MAIN PROGRAM ADJUST

ADJUSTS WEATHER TAPES OW Alf HOURLY BASIS TO REPRESENT LOCATIONS

WHICH HAVE NO WEATHER TAPES. READS IN THE YEAR FROM LOGICAL UNIT 8

AND WRITES IT OUT AT LOGICAL UNIT 9. THIS PROGRAM MAY ALSO BE USED

TO FIND THE MONTHLY AVERAGES FOR A YEAR. THEY ARE WRITTEN AT LOGICAL

UNIT 7. THE PROGRAM READS TAPES IN ENGLISH UNITS.

SUBROUTINES:

CLOUDCDAYS IN MONTHS, DELTA CLOUD, HOURLY CLOUD)

ADJUSTS CLOUD COVER ON AN MONTHLY BASIS FOR EACH HOUR

SUNNY( LATITUDE, LONGITUDE, TIME ZONE)

GETS MONTHLY DAWN AND DAY LENGTH VALUES.

DEWPNT(DRY BULB, WET BULB , BAROMETR I C PRESSURE, DEWPO INT)

FINDS HOURLY DEWPO I NT TEMPERATURES.

WETBLB(DRY BULB. WET BULB, BAROMETRIC PRESSURE, DEWPO I NT)

FINDS HOURLY WET BULB TEMPERATURES.

FUNCTIONS:

PVSF(T)

FINDS THE PARTIAL VAPOR PRESSURE OF WATER IN MOIST AIR

FOR A GIVEN TEMPERATURE.

DPF(P)

FINDS THE DEWPO I NT TEMPERATURE FOR A GIVEN VAPOR PRESSURE

WBF(H.PB)

APPROXIMATES WET BULB WHEN ENTHALPY IS GIVEN.

THE HOURLY DRY BULB TEMPERATURE, DEGREES FAHRENHEIT.

THE HOURLY DEWPOINT TEMPERATURE, DEGREES FAHRENHEIT.

THE HOURLY WET BULB TEMPERATURE, DEGREES FAHRENHEIT.

THE HOURLY WIND, SPEED KNOTS.

THE HOURLY BAROMETRIC PRESSURE, INCHES OF MERCURY.

THE HOURLY CLOUD COVER, TENTHS.

THE HOURLY TYPE OF CLOUD. OF THE LOWEST LAYER OF CLOUD,

IN NBSLD FORMAT, NOAA'S TYPES 8 AND 9 (CIRRUS) ARE DECODED AS

0.0, ALL OTHER TYPES DECODED AS 1.0

THE DOE-2 FORMAT SAME AS ABOVE, EXCEPT THAT NOAA'S TYPE

2 (STRATUS) IS DECODED AS 2.0.

THE HOURLY WIND DIRECTION, 16 POINTS.

DUMMY FOR ENTERING CURRENTLY UNUSED DATA.

lYEAR: A.D. YEAR OF WEATHER TAPE.

MN: MONTH OF WEATHER TAPE.

ID: DAY OF MONTH OF WEATHER TAPE.

IC: CITY CODE OF WEATHER TAPE.

DDBASH: THE BASE FOR CALCULATION OF HEATING DEGREE DAYS, DEG. F. THE BASE FOR CALCULATION OF COOLING DEGREE DAYS, DEG. F. MONTHLY HEATING DEGREE DAYS, (DEG. F.).

MONTHLY COOLING DEGREE DAYS, (DEG. F.).

THE DAILY AVERAGE DRY BULB TENPERATXmE, DECREES FAHRENHEIT.

THE DAILY SUM OF HOURLY DRY BULB TEMPERATURES.

THE DAILY SUM OF HOURLY DEWPOINT TEMPERATURES.

THE DAILY SUM OF HOURLY ^VET BULB TEMPERATURES.

THE DAILY SUM OF HOURLY WIND SPEEDS.

THE DAILY SUM OF HOURLY BAROMETRIC PRESSURES.

THE DAILY SUM OF HOURLY CLOUD COVERS.

THE DAILY SUM OF HOURLY DAYTIME CLOUD COVERS.

THE MAXIMUM DRY BULB TEMPERATURE FOR A DAY. DEGREES FAHRENHEIT

THE MINirm DRY BULB TEMPERATURE FOR A DAY, DEGREES FAHRENHEIT.

PSEUDONYM FOR ADBTAD

THE MINIMUM HOURLY TEMPERATURE IN A GIVEN DAY

THE MAXIMUM HOURLY TEMPERATURE IN A DAY

THE FACTOR FOR ADJUSTING HOURLY TEMPERATURES,

IF DRNGIM IS GREATER TH.4H DRNGRM. (THESE ARE LESS

THAN THE DAILY AVERAGES)

DLDRMN'S COUNTERPART FOR TEMPERATURES GREATER THAN

THE DAILY AVER4GE.

THE MONTHLY AVERAGE DRY BULB TEMPERATURE. DEGREES FAHRENHEIT.

THE MONTHLY 24fIR- AVERAGE DRY BULB TEMPERATURE.

DBT:

DPT:

WBT:

WSP:

BPR:

CCT:

TOG:

WSD

YY:

DDBASC:

DDHTRM:

DDCLRM:

DBTAD:

DBTD:

DPTD:

WBTD:

WSPD:

BPRD:

CCTD:

GCDD:

DBMAX:

DBMIN:

DBAD:

DBMNi\D:

DBMXAD:

DLDRMN:

DLDRMX:

DBTRM:

DBTRMO

B2
DEGflEES FAHRENHEIT.

DEGREES FAHRENHEIT.

INCHES OF MERCURY.

DBTRMl: THE MONTHLY MAXM IN- AVERAGE DRY BULB TEMPERATURE, DEGREES FAHRENHEIT

DBAMAX: THE MONTHLY AVERAGE MAXIMUM DAILY TEMPERATURE, DEGREES FAHRENHEIT

DBAMIN: THE MONTHLY AVERAGE MINIMUM DAILY TEMPERATURE, DEGREES FAHRE

DBMXRN: THE MAXIMUM MONTHLY TEMPERATURE, DEGREES FAHRENHEIT.

DBMNRM: THE MINIMUM MONTHLY TEMPERATURE, DEGREES FAHRENHEIT.

DFTRM: THE MONTHLY AVERAGE DEWPOINT TEMPERATURE,

WBTRM: THE MONTHLY AVERAGE VET BULB TEMPERATURE,

WSPRM: THE MONTHLY AVERAGE WIND SPEED, KNOTS.

BPRRM: THE MONTHLY AVERAGE BAROMETRIC PRESSURE,

CCTRM: THE MONTHLY AVERAGE CLOUD COVER, TENTHS.

CCCTRM: THE MONTHLY AVERAGE CIRRUS COVER, TENTHS.

CCSTRM: THE MONTHLY AVERAGE STRATUS COVER, TENTHS.

CCOTRM: THE MONTHLY AVERAGE OF OTHER CLOUD TYPES, TENTHS. (INCLUDES

STRATUS IN NBSLD FORMAT)

CCDRM: THE MONTHLY AVERAGE DAYTIME CLOUD COVER, TENTHS.

DSDRM: THE MONTHLY AVERAGE DAILY STANDARD DEVIATION, DEGREES FAHRENHEIT

DRNGRM: THE MONTHLY AVERAGE DAILY RANGE OF DRY BULB, DEGREES FAHRENHEIT

DBTIM: THE INPUT MONTHLY AVERAGE DRY BULB TEMPERATURE.

DPTIM: THE INPUT MONTHLY AVERAGE DEVPOINT TEMPERATLTRE.

WBTIM: THE INPUT MONTHLY AVERAGE VET BULB TEMPERATURE.

WSPIM: THE INPUT MONTHLY AVERAGE VIND SPEED.

BPRIM: THE INPUT MONTHLY AVERAGE BAROMETRIC PRESSURE.

CCTIM: THE INPUT MONTHLY AVERAGE CLOUD COVER.

CCDIM: THE INPUT MONTHLY AVERAGE DAYTIME CLOUD COVER.

DSDIM: THE INPUT MONTHLY AVERAGE DAILY STANDARD DEVIATION.

DRNGIM: THE INPUT MONTHLY AVERAGE DAILY RANGE OF DRY BULB.

BPRADJ: THE DIFFERENCE BETWEEN THE INPUT BAROMETRIC PRESSURE AND THE

REAL (TAPE) BAROMETRIC PRESSURE, ADDED TO EACH HOUR IN A MONTH

DH: THE DIFFERENCE IN HEIGHT BETWEEN THE SOURCE STATION AND THE

ADJUSTED LOCATION.

DLCCTM INPUT CLOUD AVERAGE AND THE

DLCCCM:

DLCCSM:

DLCCOM:

RATDPT:

RATVBT:

RATDSD:

RATDRN:

RATVSP:

CCCARR:

CCSARR:

CCOARR:

IDBCNT:

lADFLG:

IBPFLG:

IDBFLG:

THE DIFFERENCE BETVEEN THE

THE REAL CLOUD AVERAGE.

SAME AS ABOVE, FOR CIRRUS CLOUDS.

SAME AS ABOVE, FOR STRATUS CLOUDS.

SAME AS ABOVE, FOR 'OTHER CLOUDS'.

THE RATIO OF THE INPUT DEVPOINT DEPRESSION TO THE REAL ONE.

THE RATIO OF THE INPUT VET BULB DEPRESSION TO THE REAL ONE.

THE RATIO OF THE INPUT DAILY STANDARD DEVIATION TO THE REAL

STANDARD DEVIATION.

THE RATIO OF THE INPUT DAILY RANGE AND THE REAL ONE.

THE RATIO OF THE INPUT WIND SPEED AND THE REAL ONE.

HOLDS THE HOURLY CIRRUS COVER VALUES SO CLOUD CAN ADJUST THEM

HOLDS THE HOURLY STRATUS VALUES SO CLOUD CAN ADJUST THEM.

HOLDS THE HOURLY ?' OTHER CLOUD' VALUES SO CLOUD CAN ADJUST THEM

HOURS VITH TEMPERATURE 50 DEGREES LESS THAN DELIMETER.

THE FLAG FOR USING ADJUST TO GET AVERAGES ONLY.

THE FLAG FOR USING THE HEIGHT TO DETERMINE THE BAROMETRIC

PRESSURE ADJUSTMENT.

THE FLAG FOR ENTERING DRY BULB AVERAGES VHICH ARE THE AVERAGES

OF THE SITE.

MAXIMUM AND MINIMUM DAILY TEMPERATURES.

THE FLAG FOR DECIDING CLOUD AVERAGE ENTERING PROCEDURE.

THE FLAG VHICH HAS THE NU?IBER OF DAYS IN THE 12 MONTH PERIOD

THE FLAG FOR ENTERING AVERAGES IN NON-SI UNITS.

THE FLAG FOR DECIDING VET BULB DEWPOINT ENTERING PROCEDURE.

THE FLAG IffllCH HAS THE NUMBER OF DAYS SKIPPED BEFORE THE

START OF THE 12 MONTH PERIOD.

THE NUMBER OF HOURS OF DAYLIGHT FOR A DAY IN A MONTH.

THE TIME OF SUNRISE FOR A DAY IN A MONTH.

TIME OF SUNRISE.

TIME OF SUNSET.

ICCFLG:

IDYFLG:

ISIFLG:

IVBFLG:

ISKFLG:

LENGTH:

I DAW:

ISR: ISS

RLONG: THE LONGITUDE OF THE ADJUSTED LOCATION.

RLATD: THE LATITUDE OF THE ADJUSTED LOCATION.

TZN: THE TIME ZONE (STANDARD) OF THE ADJUSTED LOCATION.

NDAY: CONTAINS THE NUMBER OF DAYS IN EACH MONTH.

IH: THE HOUR OF THE DAY 5VE ARE ADJUSTING.

B3
135: C MH: THE NUMBER OF HOURS ALREADY ADJUSTED THIS MONTH.

136: C IHTOT: TOTAL NOTIBER OF HOURS IN THE YEAR.

137: C IDBTOT: NUMBER OF HOURS WITH A TEMPERATURE LESS THAN DEL I METER 49

138: C YCCS: THE SUM OF MONTHLY AVERAGES FOR STRATUS TYPE CLOUD.

139: C

140: DIMENSION DBTAD( 12,31) ,DBTIM( 12) ,DBTRM0( 12) ,DBT(24) ,DPTIM( 12)

.

141: 2 DBMXAD( 12,31) ,DBMNAD( 12,31) ,DPTRM( 12) ,DPT(24) ,VBTIM( 12) ,WBTRM( 12)

142: 3,CCDIM( 12) , CCDRM( 12) ,CCT(24) ,DRNGIM( 12) ,BPRIM( 12) ,BPRRM( 12)

,

143: 4 BPR(24) , VDR(24) , VSPIM( 12) , WSP(24) ,DSDIN( 12) , DSDRM( 12) ,T0C(24)

,

144: 5 YY(24) ,NDAY( 12) , WIiT(24) ,DRNGRM( 12) ,GCTRM( 12) ,WSPRM( 12) , 145: 6 CCCARR( 12,745) ,DBAMAX( 12) ,DBAMIN( 12) ,DBMXRM( 12) ,DBMNRM( 12)

,

146: 7 IDBCNT( 180) ,DDHTRM( 12) ,DDCLRM( 12) ,DDHTAM( 12) ,DDCLAM( 12)

,

147: 8 DBTRMK 12) , DBMXAM( 12) , DBMNAM( 12) ,DBTRM( 12) ,CCCDRM( 12)

,

148: 9 CCOARR( 12,745) , DLCCCM( 12) ,CCCTRM( 12) , CCODRM( 12) ,CGOTRM( 12)

,

149: * DLCCOM( 12) ,CCSTRM( 12) ,CCSDRM( 12) ,CCSARR( 12,745) , DLCCSM( 12)

,

150: 1 ALPHA(6)

151: DATA ALPHA /' AD' JUSTED' , ' SUMM' ,' ARIZED' , 'DEG.C. '

, 'DEG.F.

152: COMMON /DT/ IDAWN( 12) , LENGTH( 12)

153: IHTOT=0

154: C READ IN THE LATITUDE, LONGITUDE, AND TIME ZONE AND

155: C CALL SUNNY TO GET DAWN AND DAY LENGTH DATA,

156: PRINT 220

157: READ (5,210) RLATD, RLONG, TZK

158: CALL SUNNY ( RLATD, RLONG, TZN)

159: DO 10 MN= 1 , 12

160: DBMXRTK MN) =-100.0

161: DBMNRM(MN) = 100.0

162: DBMXAM(MN)=- 100.0

163: DBMKAM(MN)= 100.0

164: DO 10 MH= 1,745

165: CCCARR( MN , MH) = 1 1 .

0

166: CCOARR( MN , MH) = 1 1 .

0

167: CCSARR( MN , MH) = 1 1 .

0

168: 10 CONTINUE

169: DBMXYR=- 100.0

170: DBMNYR= 100.0

171: PRINT 230

172: READ (5,210) ISKFLG, IDYFLG

173: PRINT 240

174: READ (5,210) lADFLG

175: IF (ISKFLG. EQ.0) GO TO 30

176: C READ THE SKIPPED PORTION OF THE TAPE.

177: DO 20 IDAY= 1 , ISKFLG

178: READ (8) DBT, DPT, WET, WSP, BPR, CGT, TOC , WDR, YY, lYEAR, MR, ID, IC

179: 20 CONTINUE

180: 30 CONTINUE

181: IF ( IADFLG.EQ.0) PRINT 250

182: IF (IADFLG.EQ.0) BEAD (5,210) IBFLG.DH

183: IF (IADFLG.EQ.0) PRINT 260

184: IF (IADFLG.EQ.0) READ (5,210) IS IFLG, ICCFLG

185: IALP=6

186: IF ( IS IFLG. EQ.0. AND. IADFLG.EQ.0) IALP=5

187: PRINT 270, ALPHA( lALP)

188: READ (5,210) IDBFLG, DDBASH, DDBASC

189: IF ( lADFLG.NE.O.OR. ISIFLG.NE.0) GO TO 40

190: DDBASH=DDBASH*1 .8+32.0

191: DDBASC= DDBASC*! .8+32.0

192: C FOR EACH DAY IN THE REAL YEAR:

193: 40 DO 70 IDAY= 1 , IDYFLG

194: READ (8) DBT, DPT, WBT, WSP, BPR, CCT,TOC, WDR, YY, I YEAR, MN, ID, IC 195: DBMAX=- 100.0

196: DBMIN= 100.0

197: DBTD=0.0

198: BPPJ)=O.0

199: DPTD=0.0

200: CCCDD=0.0

201: CCSDD=0.0

B4
202: CCODD=0.e

203: CCCTD=0.0

204: CCSTD=0.0

205: CCOTD=0.0

206: CCC=0.0

207: CCS=0.0

208: CCO=0.0

209: WBTD=0.O

210: WSPD=0.0

211: NDAY(MN) = ID 212: MH=ID*24-24

213: C FOR EACH HOUR:

214: DO 50 IH= 1.24

215: C SUM THE HOURLY VALUES.

216: DBTD= DBTD+ DBT( I H)

217: DPTD=DPTD+DPT( IH)

218: DBMAX=riAX(DBMAX,DBT( IH))

219: DBMIN=MIN(DBMIN,DBT( IH))

220: BPRD=BPRD+BPR( IH)

221: C IN CLOUD ARRAYS, ALL TYPES GET 0*S IN CLEAR WEATHER.

222: C EMPTY FIELDS KEEP ll'S.

223: CT=CCT( IH)

224: IF (TOC.Ea.0.0.OR.CT.EQ.0.0) CCCARR( MN, MH+ IH) =CT

225: IF (TOC.EQ. 1.0.OR.CT.EQ.0.0) CCOARR( MN. MH+ IH) =CT

226: IF (TOC.EQ.2.0.OR.CT.EQ.0.0) CCS.ARR( MN, MH+ IH) =CT

227: IF (TOC( IH) .EQ.0.0) CCCTD=CCCTD+GT

228: IF (TOC( IH) .EQ. 1 .0) CCOTD=CCOTD+CT

229: IF (TOC( IH) .Ea.2.0) CCSTD=CCSTD+CT

230: WSPD=VSPD+1VSP( IH)

231: VBTD= WBTD+ WBT( I H)

232 : I HTOT= I HTOT+

1

233: IDB=DBT( IH)+30.5

234 : I DBCNT( I DB) = I DBCNT( I DB) +

1

235: 56 CONTINUE

236: DBMXAD(MN, ID)=DBMAX

237: DBMNAD(MN, ID)=DBMIN

238: ISR= IDAVN(NN)

239: ISS= ISR+LENGTH(NN)

240: C FOR EACH DAYTIME HOUR:

241: DO 60 IH= ISR. ISS

242: C SUM THE CLOUD VALUES.

243: IF (TOC( IH) .EQ.0.0) CCCDD=CCCDD+CCT( IH)

244: IF (TOC( IH) .EQ.2.0) CCSDD=CCSDD+CCT( IH)

245: IF (TOC( IH) .Eft. 1.0) CCODD=CCODD+CCT( IH)

246: 60 CONTINUE

247: C ADD THE DAILY AVERAGE TO THE PROPER MONTHLY AVERAGE.

248: CCCDRM(MN)=CCCDRM(MN)+CCCDD/( ISS-ISR+1)

249: CCSDRM(MN)=CCSDRM(MN)+CCSDD/( ISS-ISR+1)

250: CCODIlM(MN)=CCODRM(MN)+CCODD/( ISS-ISR+1)

251: DBTAD(MN, ID)=DBTD/24.0

252: IF ( IDBFLG. Eft. 1) DBTAD( MN, ID) = ( DBMAX+DBMIN) /2 .

0

253: IF (DBTAD(MN, ID) .LT.DDBASH) DDHTRM( MN) =DDHTRM( HN)-DBTAD( MN, ID)+D

254: 2DBASH

255: IF (DBTAD(MN, ID) .GT.DDBASC) DDCLRM( MN) =DDCLRM( MN) +DBTAD( MN , ID) -D

256: 2DBASC

257: DBTRri0(MN)=DBTRM0(NN)+DBTD/24.0

258: DBTRMK MN) =DBTRM1( MN) +( DBMAX+DBMIN) /2.0

259 : DBA]*L\X( MN ) = DBAri4X( MN ) + DBMAX

260: DBAMIN( MN)=DBAMIN(MN)+DBMIN

261: DBMXRMC MN) =MAX( DBM4X,DBriXRM(MN)

)

262: DBMNIIM( MN) =MIN( DBMIN . DBflNRfK MN) ) 263: DPTRMi MN) =DPTRM( MN) +DPTD/24 . 0

264: VSPRM( MN) =WSPRM( MTO + WSPD/24 . 0

265: CCCTnM( fIN) =CCCTRM( MN) +CCCTD/24.0

266 : CCSTrLM( MN) =CCSTRM( MN) +CCSTD/24 . 0

267: CCOTIUK MN) =CC0TTIM( MN) +CC0TD/24 . 0

268: DRNGRfK MN) = DFlNGim( MN) +DBPUX-DBMIN

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Wd 1 rull rlTi ) - Wd 1 nJli rlTI ) ^ v\ TiPMr^PM/ UilIilTiirit. MW1 rill > —- rfcPMr^PM/ Uniiljilrl*. mw* nri J /nw

OQQ • LiLiLiliKTli rlJi ) - l^L-t^Uml*. riii ) /v\ OOA < OQ 1 • ViCiUUrtni nil J - ijt*uunn< nii ) /w

OQO « LiLiL* 1 lysiK nil > - L(1>Ij 1 Kni nil / /W

OQO • tiLo 1 Kni nil ) - Lt-o 1 Kni nli ) 294 • CCOTRN( MN) =CCOTRN( MN) /N

295

CCTRM( MN) =GCGTRM( MN) +CGOTRM( MN) +CGSTRM( MR)

o/\ ^ « dtiUHnt nil ) = tiCLiUKn( rlD ) +GLiODKn( mi) +CC.&URn( rlW)

O^i'7 • <s97

!

OA t*UIi 1 lllUb OOd • PD T WT* OQA

orx ri • <S79 ! rKlWl IKhi h^XlKLnK IjLnrhKAlUKJli 1>A1A rOK rut. KCiALt YrJul. O AA • uu s>u nil- 1 , U

o«J 1 • rKlii 1 , UDAnAX( Mil ) « DBAnllil PITO , UDnXKni nn) , UDrinKPlt nn ) « UDnlKni nW

O AO • ^) , DDLLKnv rill } OAO • r^OWT T fJTTT ljUn X inutj

OA>t. • oW4

!

r 1 nu 1 ounnHiK Anu w in i ilk uil& i^n o i a i lo 1 11^9

O A R • T\i\ JJU IWW 1 AA T1-— 11

1 PA , lov

OA£ « 1 Ud lU 1 - 1 Ud lU 1+ 1 UdIjIi 1 1 1 i O/i "T I oM7

!

11/Dljn l \ 1 J - U

OAO • J-r— 1—T_AA4V

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O 1 o • Ir I 1 Ud lU 1 . Li 1 . OUU i UDUL&0-%i

O 1 AIT! Ir 1. lUrJlUl.Ll.l vsXiXJ i } f UDULOO-o

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11? \ IUd lUl.Ll.l IJl l\j 1 lU J ) UDUrJb^ - o o lo IV K iUUlUl.Ll. lrilUl> UDnAiK-J

o 19 10W con 1 1 ri ut, 320 DD T WT TTTt? M r\lVT* rKini IrLLn UU 1 . 32 1 DD I MT* TiDMWD i\Drkrcfi npniTCQ rfcPT*l?C^ TkRniTGA T^RMNVP nRrHTG^J HHTIP'^I rHI n 1 oU0 , UDnXYK, UdUHiO*! « UdULoo « UdUHjHc « UdUHj&o « uonn in» uduxl.0«s « UDUCiOO

322 2 , DBDES4 , DBDES5

323 REVIND 8

324 IF ( ISKFLG.EQ.0) GO TO 120

325 C READ THE SKIPPED PORTION OF THL lAl't. 326 DO 110 IDAr= 1 , ISKJ' LG

327 READ (8) DBT.DPT, VBT, VSP,BPR,CCT,TOC,VDR. YY, IYEAR,MN, ID, IC 328 1 10 GONTINUE

329 120 CONTINUE

330 C FOR EACH MONTH:

331 DO 140 MN= 1 , 12 332 c FIND THE MONTHLY AVERAGE DAILY STANDARD DEVIATION.

333 N=NDAY( MN)

334 DO 130 IDAY=1,N

335 DSDRM( MN) = DSDRM( MN) +( DBTAD( MN . I DAY) -DBTRM( MN) ) **2

B6
336: 130 CONTINUE

337: DSDRM(MN)=SQRT(DSDRM(MN)/(N-1))

338: 140 CONTINUE

339: IF ( IADFLC.EQ.0) PRINT 310

340: IF ( IADFLG.EQ.0) READ (5.210) IWBFLG

341: IF ( IADFLG.Ea.0) PRINT 320

342: IF ( IDBFLG.EQ.0) PRINT 330

343: IF ( IDBFLG. EQ. 1) PRINT 340

344: IF ( IADFLG.EQ.0. AND. ISIFLG.EQ.0) PRINT 350

345: IF ( lADFLG.Eft. l.OR. ISIFLG.EQ. 1) PRINT 360

346: YGCS=0.0

347: C FOR EACH MONTH:

348: DO 150 MN= 1 , 12 349: C CHECK TO SEE IF THERE IS A CATEGORY FOR STRATUS.

350: YCCS=YCCS+CCSTRM(MN)

351: C READ IN THE AVERAGES FOR THE ADJUSTED YEAR.

352: C IF CALCULATIONS ARE NEEDED, MAKE THEM.

353 : CCPRWT= CCDRM( MN

)

354: IF ( ICCFLG.EQ.0) CCPRNT=CCTRM( MN)

355: VRITE (7.420) MN, DBTRM0( MN) , DBTRMl ( MN) .CCPRNT, ¥SPRM( MN) , DPTRM( MN

356: 2) ,BPRRM(MN) ,DSDRM(MN) ,DRNGIIM(MN) , WBTRM( MN)

357: IF ( IDBFLG. EO.O) PRINT 370, MN, DBTRM0( MN) , CCPRNT, WSPRMC MN) . DPTRM

358: 2( MN) , BPRRM( MN) , DSDRM( MN) , DRNGRM( MN) , WBTRM( MN)

359: IF ( IDBFLG. EQ. 1) PRINT 370, MN, DBTRMl ( MN) , CCPRNT, WSPRM( MN) , DPTRM

360: 2(MN) ,BPRRM(MN) ,DSDRM(MN) ,DRNGRM(MN) ,WBTRM(MN)

361: IF ( IADFLG.NE.0) GO TO 150

362: READ (5,210) DBTIM( MN) , CCDIM( MN) , WSP IM( MR) , DPTIM( MN) , BPRIM( MN) ,

D

363: 2SDIM(MN) ,DRNGIM(MN) ,WBTIM(MN)

364: DLCCGM( MN) = ( CCD IMC MN) -CCDRMC MN) ) *CCCTRM( MN) /CCDRM( MN)

365 : DLCCSM( MN) = ( CCD IM( MN) -CCDRM( MN) ) *CCSTRM( MN) /CCDRM( MN)

366 : DLCCOM( MN) = ( CCD I M( MN) -CCDRMC MN) ) *CCOTRM( MN) /CCDRM( MN)

367: IF (DSDIM(MN) .EQ.0.)DSDIMCMN)=DSDRMCMN)

368: IF (ICCFLG.EQ.0) DLCCCM( MN) = ( CCDIMC MN) -CCTRM( MN) ) *CCCTRM( MN) /CCT

369: 2RM(MN)

370: IF (ICCFLG.EQ.0) DLCCSMC MN) = ( CCDIMC MN) -CCTRMC MN) ) *CCSTRM( MN) /CCT

371: 2RM(MN)

372: IF (ICCFLG.EQ.0) DLCCOM( MN) = ( CCDIM( MN) -CCTRM( MN) ) *CCOTRM( MN) /CCT

373: 2RM(MN)

374: IF ( ISIFLG.NE.0) GO TO 150

375: DBTIM(MN)=DBTIM(MN)*1. 8+32.0

376: DPTIM(MN)=DPTIM(MN)* 1.8+32.0

377: DRNGIM(MN) =DRNGIMC MN)*1 .8

378: WBTIMCMN)=WBTIMCMN)* 1.8+32.0

379: DSDIM(MN)=DSDIMCMN)*1. 8+32.0

380: VSPIMCMN)=VSPIMCMN)*3.6/1.853

381: BPRIM(MN)=BPRIMCMN)*29. 92/101. 1 382: 150 CONTINUE

383: IF (IADFLG.NE.0) GO TO 200

384: C CALL CLOUD TO ADJUST THE CLOUD VALUES FOR THE YEAR.

385: CALL CLOUD ( NDAY, DLCCCM, CCCARR)

386: IF ( YCCS.NE.0.0) CALL CLOUD ( NDAY, DLCCSM, CCSARR)

387: CALL CLOUD ( NDAY, DLCCOM, CCOARR)

388: READ (8) DBT, DPT, TOT, WSP , BPR, CCT, TOG, WDR, YY, lYEAR, MN, ID, IC 389: C FOR EACH MONTH:

390: DO 170 M= 1, 12 391: C GET THE MONTHLY RATIOS.

392: RATDPT=(DBTrM(riN)-DPTIM(MN) ) /( DBTRMC MN) -DPTRMC MN) ) 393 : RATVBT= C DBT I M( MN ) - ^VBT I iM( MN) ) / ( DBTRMC MN ) - ivBTRMC MN ) ) 394: BPRADJ=BPRIM( fIN)-BPRRMC MN)

395: RAT^^SP=VSPIMC NN)/WSPRMC MN)

396: DLDBTM=DBTIMC MN)

397: RATDSD=DSDIMC riN)/DSDRMC MN)

398 : DLDRNG= C DR^NG I MC MN ) -DRNGRMC MN ) ) /2 .

0

399: RATDRrJ=DRNGIMC MN) /DRNGRMC MN)

400: N=NDAY(MN)

401: DBAMAXCfIN)=0.0

B7
402: DBAMIN(MN)=e.0

403: C FOR EACH DAY:

404: DO 170 IDAY=1,N

405 : DBAD= DBTAD( MN , I D)

406: C GET THE DAILY DRY BULB ADJUSTMENT.

407: IF (DBAD.NE.DBriXAD(MN, ID) ) DLDRMX= l+DLDRNG/( DBPD{AD( MW, ID) -DBAD)

408: IF (DBAD.NE.DBMNAD(MN, ID) ) DLDRMN= l+DLDRNG/( DBAD-DBMNAD( MN, ID)

)

409 : DLDBTD= ( DBAD-DBTRM( MN) ) *RATDSD+DLDBTM

410: IF (DLDBTD.LT.DDBASH) DDHTAM( MN) =DDHTAM( MN) -DLDBTD+DDBASH

411: IF (DLDBTD.GT.DDBASC) DDCLAM( MN) =DDCLAM( MN) +DLDBTD-DDBASC

412: MH= IDAY*24-24

413: DBMAX=-100.0

414: DBMIN= 100.0

415: C FOR EACH HOUR:

416: DO 160 IH=1,24

417: C FIND THE HOURLY DRY BULB. DEWPOINT, AND VET BULB ADJUSTM

418: DB=DBT( IH)

419: DLDBTH= DB-DBAD

420 : DLWBTH= ( WBT( IH) -DB) *RATVBT

42 1 : DLDPTH= ( DPT( IH) -DB) *RATDPT

422: IF (DLDRNG.LT.0.0) DLDBTH=DLDBTH*RATDRN

423: C IF DAILY RANGE MUST BE INCREASED, AN ADDITIVE

424: C ADJUSTMENT IS MADE TO THE DAILY EXTREMES. OTHERWISE

425: C RATDRN IS USED.

426: IF (DLDRNG.GT.0.0. AND.DLDBTH.GT.0.0) DLDBTH= DLDBTH*DLDRNX

427: IF (DLDRNG.GT.0.0. AND.DLDBTH.lt. 0.0) DLDBTH= DLDBTH^DLDRMN

428: DB=DLDBTH+DLDBTD

429: C ADJUST THE HOURLY VALUES, IF NECESSARY CALL DEWNT OR VET

430 : DBMAX= MAX( DB . DBMAX)

431: DBMIN=MIN(DB,DBMIN)

432: IDB=DB+50.5

433: IDBCNT( IDB)= IDBCNT( IDB)+1

434 : WBT( I H) = DB+DLWBTH

435 : DPT{ I H) = DB+ DLDPTH

436: IF ( IBPFLG.EQ.0) BPR( IH) =BPR( IH) +BPRADJ

437: IF ( IBPFLG.Eft. 1) BPR( IH) =BPR( IH)/10**(DH/( (459.+(DBT(IH)+DB)/'2

438: 2.)*122.8))

439: DBT(IH)=DB

440: IF ( IWBFLG.EQ.0) CALL DEVTPNT ( DBT( IH) , WBT(IH) , BPR( IH) , DPT( IH)

)

441: IF ( IWBFLG.EQ. 1) CALL WETBLB ( DBT( IH) , DPT( IH) , BPR( IH) , VBT( IH)

)

442: CCT( IH) =MOD( ( CCCARR( MR, MH+ IH) +CCOARR( MN, MH+ IH) +CCSARR( MN, MH+ IH

443: 2)), 11.0)

444: VSP( IH)=VSP( IH)*RATWSP

445: 160 CONTINUE

446: C WRITE OUT A DAY OF OUR ADJUSTED YEAR.

447: DBAMAX(MN)=DBAMAX(MN)+DBMAX

448: DBAMIN(MN)=DBAMIN(MN)+DBNIN

449 : DBMXAMC MN) = MAX( DBMAX, DBMXAM( MN)

)

450: DBMNAfK MN) =MIN( DBMIN , DBMNAM( NN)

)

451: WRITE (9) DBT. DPT, WBT, WSP , BPR, CCT, TOC , WDR, YY, T YEAR, MN, ID, IC 452: READ (a,END=l70) DBT, DPT, WBT, WSP , BPR, CCT, TOC , V/DR, YY, I YEAR, MN , ID,

453 : 2 IC 454: 170 CONTINUE

455: IDBTOT=0

456: C FIND THE DESIGN STATISTICS FOR THE ADJUSTED YEAR.

457: DO 180 1= 1 , 180

458: IDBTOT= IDBTOT+ IDBCNT( I) 459: J=I-49

460: IF ( IDBTOT.LE.0) DBMNYR=J

461: IF ( IDBTOT.LT. 10) DBDES2=J

462: IF ( IDBTOT. LT.O0) DBDES3=J

463: IF ( IDBTOT.LT. 200) DBDES4=J

464: IF ( IDBTOT.LT. 500) DBDES5=J

463: IF ( IDBTOT. LT. ( IHTOT-5G0) ) DBDES6=J

466: IF ( IDBTOT. LT. ( IHTOT- 200) ) DEDES7=J

467: IF ( IDBTOT. LT. ( IHTOT-50) ) DBDES8=J

468: IF ( IDBTOT. LT.( IHTOT- 10) ) DBDES9 = J

B8
469: IF ( IDBTOT.LT. IHTOT) DBMXYR=J

476: 186 CONTINUE

471: PRINT 380, ALPHA( 1) ,ALPHA(2)

472: PRINT 390

473: DO 190 MN= 1 , 12

474: N=NDAY(MN)

475: DBAMAX(MN)=DBAMAX(MN)/N

476: DBAMIN( MN) =DBAMIN( MN)/'N

477: PRINT 400 , DBAMAX( MN) , DBAMIN( MN) , DBMXAM( MN) , DBMNATK MR) . DDHTAM( MN

478: 2) ,DDCLAM(MN)

479: 196 CONTINUE

486: C PRINT THE EXTREME TEMPERATURE DATA FOR THE ADJUSTED YEAR.

481: PRINT 410, DBMXYR, DBDES9 , DBDESS, DBDES7 , DBDES6 , DBMNYR, DBDES2 , DBDES3

482 : 2 , DBDES4 , DBDESS

483: 266 CONTINUE

484: IF ( lADFLG.EQ. 1) PRINT 386, ALPHA(3) ,ALPHA(4)

483 : STOP

486: G

487: G

488: G

489: G

496 : 216 FORMAT ()

491: 226 FORMAT C ENTER THE LATITUDE, LONGITUDE, AND THE TIME ZONE.')

492: 236 FORMAT (

' ENTER THE NOTIBER OF DAYS TO BE SKIPPED BEFORE THE START'

493: 2,/,' OF THE 12 MONTH PERIOD BEING USED',/' FOLLOWED BY THE ', 494: 3 'NUMBER OF DAYS IN IT')

495: 246 FORMAT (' ENTER 0 IF AN ADJUSTED YEAR IS TO BE MADE ELSE ENTER 1')

496: 256 FORMAT (' ENTER 0 0.0 IF BAROMETRIC PRESSURE DATA IS AVAILABLE',/,

497: 2 ' OTHERWISE ENTER 1 AND THE HEIGHT DIFFERENCE OF THE NEW ',/,

498: 3 ' AND OLD LOCATION H( NEW) -H( OLD) IN FEET')

499: 260 FORMAT (' ENTER 0 IF USING SI UNITS (FOR INPUT OF CLIMATE'./,

500: 2 ' VARIABLES ONLY), ELSE ENTER 1',/,' FOLLOWED BY THE CLOUD FLAG:'

501: 3,/,' 1 IF USING DAYTIME AVERAGES',/,

502: 4 ' 0 IF USING FULL DAY AVERAGES.')

503: 276 FORMAT (

' ENTER 0 IF THE 24-HOUR DRY BULB AVERAGE IS TO BE',/,

504: 2 ' USED ENTER 1 IF THE MAX-MIN AVERAGE IS TO BE USED ' ,/,

505: 3 ' (THE AVERAGE WHICH IS CHOSEN WILL BE USED IN THE',/.

506: 4 • DEGREE DAY , DAILY STANDARD DEVIATION AND ALL OTHER DRY',/,

507: 5 ' BULB CALCULATIONS) ' ,/.

508: 6 ' THEN ENTER THE BASE TEMPERATURES" TO BE USED IN ',/,

509: 7 ' THE DEGREE DAY CALCULATIONS IN •,A6. ':',/,

510: 8 ' FIRST HEATING. THEN COOLING.',/)

511: 280 FORMAT (/./.' INPUT YEAR SUMMARY' ,/,

512: 2 • AVG. MAX AVG. MIN EXT. MAX EXT. MIN D.D. HEAT D.D. COOL')

513: 290 FORMAT ( 4F9 . 3 , 2F 10. 3)

514: 300 FORMAT (' SUMMER DESIGN 0 HRS:',F5.0,' 16 HRS:',F5.0.' 50 HRS: '

,

515: 2 F5.0,' 200 HRS:',F5.0,' 500 HRS: ' . F5. 0, /, ' WINTER DESIGN 0 HRS:',

516: 3 F5.0,' 10 HRS:'.F5.0,' 50 HRS:',F5.0,' 200 HRS:',F5.0,' 500 HRS:'

517: 4.F5.0)

518: 310 FORMAT (' ENTER 0 IF USING WET BULB ONLY. AS FROM AIR FORCE',

519: 2 * MANUAL 88/29',/, ' ENTER 1 IF USING DEWPOINT ONLY, AS FROM',

520: 3 ' NOAA CLIMATIC ATLAS',/,' ENTER 2 IF USING BOTH')

521: 320 FORMAT (' ENTER THE WEATHER AVERAGES TO ADJUST THE TAPE.',/,

522: 2 ' EVEN IF VALUES CANNOT BE FOUND OR ARE NOT USED',/,

523: 3 ' SOMETHING MUST BE ENTERED FOR ALL 96 VALUES',/.

524: 4 ' ( ENTER ONLY ONE TYPE OF DRY BULB AVERAGE.)',/,

525: 5 ' IF NECESSARY ECHO THE VALUES FROM THE INPUT YEAR',/)

526: 330 FORMAT (' MONTH DRY BULB CLOUD WIND DEW BAROMETRIC DAILY DA

527: 2ILY ',' WET',/,9X,' 24-HR COVER SPEED POINT PRESSURE S.D. ' 528: 3, 'RANGE BULB')

529: 340 FORMAT (' MONTH DRY BULB CLOUD WIND DEW BAROMETRIC DAILY DA

530: 2ILY ',' WET'./,9X, ' MAXMIN COVER SPEED POINT PRESSURE S.D. ' 531: 3, 'RANGE BULB')

532: 350 FORJLAT (9X, ' DEG.C. TENTHS M/S DEG.C. KPAS ', 533: 2 'DEG.C. DEG.C. DEG.C.')

534: 360 FORMAT (9X, ' DEG.F. TENTHS KNOTS DEG.F.

B9
535: 2 'IH.HG. DEG.F. DEG.F. DEG.F.*)

536: 370 FORMAT (14 5X F7 2 3X F5 2 . F7. 2. F6 . 2 . F9 . 2. F7 . 2. F7 2 F7 2>

537: 380 FORMAT (2A6,' YEAR COMPLETE.*)

538: 390 FORMAT (/./,' ADJUSTED YEAR SUMMARY'

539: 2 ' AVG. MAX AVG. MIN EXT. MAX EXT. MIN D.D. HEAT D.D, COOL')

540: 400 FORMAT (4F9.3,2F10.3)

541: 410 FORMAT ( • SUMMER DESIGN 0 HRS: ' ,F5.0, ' 10 HRS: ' ,F5.0, ' 50 BBS: '

,

542: 2 F5.0,' 200 HRS:'.F5.0,' 500 HRS: ' ,F5.0,/, ' WINTER DESIGN 0 HRS:'

543: 3 F5.0,

10 HRS:',F5,0.' 50 HRS:*,F5.0,' 200 HRS:',F5.0,' 500 HRS:

544: 4,F5.0)

545: 420 FORMAT ( I4,2F7.2,3X,F5.2,F7.2,F6.2,F9.2,F7.2,F7.2,F7.2)

546: C

547: END

EOF AT LIRE 547

BIO
APPENDIX C: Listings of the FORTRAN Subroutines and Functions

used by the ADJUST and SELECT ProgramsSubroutine CLOUD (A)*

Subroutine DAYW(S)*

Subroutine DEWNT(A)

Subroutine DEWPNT(S)

Subroutine ORDER(S)

Subroutine SLYEAR(S)

Subroutine SUNNY (A, S)

Subroutine WETBLB(A)

Function DPF(A,S)

Function PVSF(A)

Function PVSF(S)

Function WBF(A)

* A=Needed by the ADJUST Program.

S=Needed by the SELECT Program.

CI
l: 2: C SUBROl

3: C ADJUST

4: C UNITS

5: C A RANE

6: C

7: C INPUT

8: C N

9: C D

10: C C

11: C LOCAL

12: C N.v^N . 1/ • iiuL, iiuriDt,n yjt lOAl i rrt: Ut' TJiWTUS.

13: C NTP (TYPE+1, NUMBER) : THE HOURLY POSITION OF THIS TENTH.

14: C NCP(TYPE+1 .NUMBER) : THE RANDOM NTP.

15: C NUMBER: THE NUMBER OF HOURS IN A MONTH.

16: C MN: THE MONTH.

17: G CCT: STORES A CLOUD VALUE FOR EASIER HANDLING.

10: G lADJ: THE NUMBER OF UNIT ADJUSTMENTS WHICH MUST BE MADE TO

19: G CHANGE THE AVERAGE BY DELCCT, DIVIDED BY TEN.

20: C

21: C DIMENSION THE ARRAYS ALL TYPES BY NAME.

22: DIMENSION NTC( 13) ,NTP( 13,745) ,NCP( 13,745) ,CCTARR( 12,745)

,

23: * NDAY(12) ,DELCCT( 12)

24: C FOR EACH MONTH:

25: DO 190 MN= 1, 12 26: G FIND THE NUMBER OF HOURS AND THE ADJUSTMENT NEEDED.

27: NUMBER=NDAY( MN) *24

28: DELTAC=DELCCT( KN)

29: G INITIALIZE THE CLOUD TYPE COUNTER.

30: DO 10 1=1, 13 31: NTC( I)=0

32: 10 CONTINUE

33: G FOR EACH HOUR:

34: DO 70 1= l,NniBER

35: G FIND THE TENTH OF CLOUD VE ARE DEALING WITH AND

36: C INCUEIIENT THE COUNT AND STORE THE HOUR.

37: CCT=CCTARR(MN, I) 33: IF (CCT. NE. 10.0) GO TO 30

39: IF (CCTARR(rm, I-l) .NE. 10. .OR.CCTARR(MN, I+l) ,NE. 10. )G0 TO ! 40: NTC( 12)=N1X]( 12) + 1 41: 1 12=NTC( 12)

42: NTP( 12, 112)=

I

43: GO TO 70

44: 20 NTC( 11)=NTC( 11)+1

45: 1 1 1 = NTC( 11)

46- NTP( 11, 1 11) = I 47 GO TO 70

48 30 IF (CCT.NE.0.0) GO TO 50

49 IF (CCTARR(MN, I-l) .NE.0. .OR.CCTARR(MR, I+l) .NE.0.)GO TO 40

50 NTC( 13)=NTC( 13) + 1 51 I 13=NTC( 13)

52 NTP( 13, 1 13) = I 53 GO TO 70

54 40 NTC( 1)=NTC( 1)+1

55 I1=NTC( 1) 56 NTP( 1, I1)=I

57 GO TO 70

58 50 DO 60 IC=2, 10 59 R= IC-1

C2
60 IF f PPT NV Ht HA TTb 6

1

NTT" f I P 1 = NTP f I P t + 1 62

63 NTPf If* iir> = i 64 fin TYI 7fl 65 60

66 70 PONT 1 NITF V 1 C iron IT APR ff fkTm nrwrn tvoh** 68 nn 1 CkCi 1=1 1 <i 69 c fiPl? TTflLT MANV HIT TTI AT" TVDI? TTIirill? AVm

70 rUtiUlU PFRMim? XHTT T P PfIG 1 T* I ONC < Pi^f TDC ^ It* liLcjItl rU& i 1 iUIHo i UUUn^^ • 7

1

N=NTf1" 11 11 i Vi V *l / 72 IC=0

73

74 IC= IC+ 1 75

76 75 uVll I 111 Vlli 77 DO R() T = 2 N 7

78 IC= IC+

1

79

80 80 uvpii 1 J 11 vr^ 81 DO R5 l = !l N 7

82 IC= IC+

1

83 NCP(.I Ii\>l \ *J ^ n=NTP( 1/11 11 VvfI TDx\J f 84 85 CONT I NUE

85 DOX/v 7V I'Tyllfi 1-4 N 7

86 IC= IC+

1

87

88 90 CONTINUE uvli I. All 89 DO 95 1=5, N,

7

90 IC= IC+

1

91 NCP(J I)=NTP(J IC)

92 95 CONTINUE XaluT Al 1 1 11 \JM^ 93 DOXF\J 96W 1=6 I Vfllfi N 7

94 IC= IC+

1

95 NCP( Al X\VfA/ J.I) =NTP( lill\%/yl J IC)U / 96

96 uVil 1 1 11 vL< 97

!

DOA/v 100 1 W 1=7 I f f N11 f 7% IC= IC+

1

99

NCPf.I ll\jl V«Jf I)=NTP(J 1/1111 \ %J % IC) lU/

AW A W PONT \^\/ll 1 T1 NTIF 11 LflLf XV 1 < nJJU ADD ONwll THF 1 fn fiTRINH ^ 1 IV 1 11 \7 TFNS 1 Cdll 1^ miAND1/ 7RROES Jul^lLvllji^ TO1 V 'I'HK A III* N1 = NTr( 1 1

)

1 NPsNTVf 11^ 11 A 11>t

1 Ctd. nn 1 1 ct T = 1 NS

11 N11 1 = N 1 1 1 11 1 ~ A NCPf llUlT V 111)111/ 1 N1)=NCP(12 11\J1 \ 1 M 4 T)1 /

1 ft? 1A 10Av CONT I NIIF 103 NTC ( 1 1 ) = N

1

1111^\ 11/111

11 CIQ V y N1 =NTr< 1

)

11A111VJ\ 1/

1AAVlet NO=NTP( Ift) 111 All nnU\J 19A 1 1=1 A 1 y N2llM

1 1 «2 N1=N1+

1

1A 111O NCP( 11\^1 \ 11 4 N1)=NCP(13 11 1 / llul \ 1 W f I)1 / A A Y 1A P(t £tV CONT I NUE

1 15 c FIND HOW MANY ADJUSTMENTS MUST BE MADE.

116 NTC( 1)=N1

117 IADJ= ABS( DELTAC*RUMBER/ 10 . 0) + .

5

lie IF (DELTAC) . 180. 150

119 IF ( IADJ.GT.(NTC( ll)/2)) PRINT 600 MN

120 DO 140 1=11,2,-1

121 N2=NTC( I-l)

122 N1=NTC( I)

C3
123: NADJ=MIN( lADJ.Nl)

124: DO 130 J=1,NADJ

125: N2=N2+1

126: IP=NCP(I.J)

127: CCTARR(MK, IP)=I-2

128: NCP( I-1,N2)=IP

129: 130 CONTINUE

130: NTC(I-1)=N2

131: 140 CONTINUE

132: GO TO 180

133: 150 IF ( IADJ.GT.(NTC( l)/'2)) PRINT 660 MN

134: DO 170 1=1, 10 135: N2=NTC(I+1)

136: R1=NTC(I)

137: NADJ=MIN( lADJ.Hl)

138: DO 160 J=1,NADJ

139: R2=N2+1

140: IP=NCP(I,J)

141; CCTARR(Mri, IP) = I 142: HCP( I+1,N2)= IP 143: 160 CONTINUE

144: NTC(I+1)=N2

145: 170 CONTINUE

146: 180 CONTINUE

147: 190 CONTINUE

148: 600 FORMAT (' f WARNING! CLOUD ADJUSTMENT FOR MONTH' , 13,

149: * IS EXCESSIVE FOR THE ALGORITHM.')

150: RETURN

151: END

Subroutine DAYW(S)

1 : SUBROUTINE DAYW ( MONTH, IDAY, DBTP, DRNG, CCT, CCAD, DDHT, DDCL,

2: * DEW,WETB,WNSP, IYEAR,K. IN1YR,L0GUNI,BH,BC,BAPR, IDBFLG)

3: C

4: C SUBROUTINE TO READ A DAY FROM A FILE CONTAINING A VEATHER TAPE

5: C CALCULATE AVERAGES AND OTHER VALUES AND RETURN THEM TO MAIN.

6: C WRITES THE DAY INTO OUR DEFINE FILE.

7: C

8: C OUTPUT VARIABLES:

9: C MONTH MONTH OF THE YEAR.

10: C IDAY DAY OF THE MONTH.

11: C DBTP THE AVERAGE DRY BULB TEMPERATURE FOR THE DAY DEG. C.

12: C DRNG THE DAILY DRY BULB RANGE DEG. C.

13: C CCT THE AVERAGE CLOUD COVER IN TENTHS.

14: C CCAD THE AVERAGE DAYTIME CLOUD COVER IN TENTHS.

15: C WNSP THE AVERAGE WINDSPEED FOR THE DAY M / S.

16: C lYEAR THE PLACE THIS YEAR WAS INPUT, FROM 1 TO 10.

17: C DDCL DEGREE DAYS COOLING BASED ON BC DEG. C. IN DEG. C. * DAYS.

18: C DDHT DEGREE DAYS COOLING BASED ON BH DEG. C. IN DEG. C. * DAYS.

19: C DEWP THE AVERAGE DEWPOINT TEMPERATURE, DEG. C.

C4
C WETB THE AVER.*.GE WET BULB TEMPERATURE, DEC. C. C BAPR THE AVERAGE BAROMETRIC PRESSURE, KILOPASCAL.

C LOCAL VARIABLES:

C YY USED TO READ IN UNUSED WEATHER DATA.

C DB HOURLY DRY BULB TEMPERATURES DEC. F. C CC HOURLY CLOUD COVER VALUES.

C DPT HOURLY DEWPOINT TEMPERATURES DEC. F. C WBT HOURLY WET BULB TEMPERATURES DEC. F. C PBT HOURLY BAROMETRIC PRESSURE VALUES INCHES OF HG. C WST HOURLY WIND SPEEDS KNOTS.

C I DAWN APPROXIMATE HOUR OF DAWN FOR EACH MONTH.

C LENGTH HOURS OF SUNLIGHT FOR EACH MONTH.

C DAYMIN THE MINIMUM DRY BULB TEMPERATURE FOR THE DAY DEC. F. C DAYMAX THE MAXIMOTI DRY BULB TEMPERATURE FOR THE DAY DEC. F. C IDBFLG 1 IF THE MAX-MIN AVERAGE IS TO BE USED, 0 FOR 24HR

C AVERAGE.

C INPUT VARIABLES:

C BH THE BASE FOR THE DEGREE DAYS HEATING ( DDHT) , DEC. C. C BC THE BASE FOR THE DEGREE DAYS COOLING (DDCL), DEC. C. C K POSITION OF THE DAILY DATA IN OUR DEFINE FILE.

C INIYR DIFFERENCE BETWEEN THE FIRST YEAR USED FROM THIS

C TAPE AND THE NUMBER OF ITIARS ALREADY USED.

C LOGUNl LOGICAL UNIT FROM WHICH THE TAPE IS READ. C

C DECLARE THE DIMENSIONS (ALL TYPES BY NAME).

DIMENSION YY(72) ,DB(24) ,CC(24) ,PBT(24) ,WST(24) ,DPT(24) ,WBT(24)

COMMON /DT/ IDAWN( 12) ,LENGTH( 12)

C READ IN THE RECORD FOR A FULL DAY AND WRITE IT IN C IN THE DEFINE FILE.

READ (LOGUNl ,END= 30) DB, DPT, WBT, WST, PBT, CC , YY, I YEAR, MONTH,

* IDAY, ICITY

K=K+1

IYEAR= IYEAR-INIYR+1

WRITE (10'K) DB, DPT, WBT, WST, PBT, CC, YY, IYEAR,MONTH, IDAY. ICITY

C INITIALIZE THE SWIMING VARIABLES.

WNSP=0.0

DEWP=0.0

WETB= 0 .

0

DBTP=0.0

CCT=0.O

CCAD=0.O

DDHT=0.0

DDCL=0.0

BAPR=O.0

C FOR EACH HOUR:

C ADD IN THE VALUES TO BE AVERAGED.

C CHANGE THE MINIMUM AND MAXINUPI AS NECESSARY.

DO 10 J=l,24

DEWP=DPT( J)+DEWP

WETB= WBT( J ) +WETB

BAPR=PBT( J)+BAPR

DBTP=DBTP+DB( J)

WNSP=WNSP+WST( J)

CCT=CCT+CC( J) IF (J.EQ. 1) DAYMAX=DB(J)

IF (J.EQ. 1) DAYMIN=DB(J)

DAYMAX= MAX( DAYMAX, DB( J )

)

DAYMIN=MIN( DAYMIN, DB( J)

10 CONTINUE

C DIVIDE THE SUMS TO GET AVERAGES, FIND THE RANGE, AND

C CONVERT TO SI UNITS.

DRNG= ( DAYMAX-DAYMIN) *5/9

DBTP= ( DBTP/24 . 0-32 . 0) *5/9

IF ( IDBFLG. EQ. 1) DBTP=(DAYMIN+DAYMAX-64.0)*5/18

CCT=CCT/24.0

BAPR= ( BAPR/24 . 0) * 10 1 . 1/29 . 92

1

DEWP= ( DEWP/24 . 0-32 . 0) *5/9

WETB= ( WETB/24 . 0-32 . 0) *5/9

WNSP= WNSP/24 . 0* 1 . 853/3 .

6

C5
89: C FOR EACH SUNLIT HOUR SUM THE CLOUD COVER.

90: 11= IDAVn( MONTH)

91: 12= I 1+LENGTH(M0NTH)

92: DO 20 1= I 1 , 12 93: CCAD=CCAD+CC( I) 94: 20 CONTINUE

95: CCAD=CCAD/(LENGTH( MONTH) +1)

96: C FIND THE DEGREE DAYS.

97: IF (DBTP.GT.BC) DDCL=DBTP-BC

98: IF (DBTP.LT.BH) DDHT=BH-DBTP

99: 30 RETURN

100: END

Subroutine DEWPNT (A)

1: SUBROUTINE DEWPNT ( DB, WB, FB, DP)

2: C

3: C SEE NBS BUILDING SCIENCE SERIES 69, NBSLD, PAGE 32D.

4: C

5: PVP=PVSF(VB)

6: IF (DB-WB) 10, 10, 7: VSTAR=0.622*PVP/(PB-PVP)

8: IF ( WB-32. ) , ,20

9: PV=PVP-5.704E-4*PB*(DB-WB)/1 .8

10: GO TO 30

11: 10 PV=PVP

12: GO TO 30

13: 20 CDB=(DB-32. )/l .8

14: CWB=( WB-32. )/1.8

15: HL=597.31+0.44O9>':CDB-CWB

16: CH=0. 2202+0. 4409*WSTAR

17 : EX= ( VSTAR-CH*( CDB-CVB) /HL) /0 . 622

1 8 : PV= PB»EK/ ( 1 . +EX)

19: 30 IF (PV.LE.0) GO TO 50

20: IF (DB.NE.WB) GO TO 40

21: DP=DB

22: GO TO 50

23: 40 CONTINUE

24: DP=DPF(PV)

25 : 50 RETURN

26 : END

Subroutine DEWPNT (S)

1: SUBROUTINE DEWPNT ( DB, WB, PB, DP)

2: C

3: C SEE NBS BUILDING SCIENCE SERIES 69, NBSLD, PAGE 32D.

4: C

5: PVP=PVSF(WB)

6: IF (DB-WB) 30,30, 10

7: 10 WSTAR=0.622*PVP/(PB-PVP)

8: IF (WB) 20,20,40

9: 20 PV=PVP-5.704E-4*PB*( DB-WB)

10: GO TO 50

11: C

12: 30 PV=PVP

13: GO TO 50

C6
14: C

15: 40 HL=597.31+0,4409*DB-WB

CH=0. 2402+0. 4409*WSTAR

EX=(WSTAR-CH*(DB-WB)/HL)/e.622

18: PV=PB*EX/( 1 .+EX)

19 50 IF (PV.LE.0) GO TO 70

20: IF (DB.NE.WB) GO TO 60 21: DP=DB

22: GO TO 70

23: C

24: 60 CONTINUE

25: PV=PV*29.92/101.

1

26: DP=(DPF(PV)-32.0)/1.8

27: 70 RETURN

28: C

29: END

Subroutine ORDER (S)

12 C

3 C

4

5 c

6 c

7 c 8 c

9 c

10 c

11 c

12 c

13 c

14 c

15 c

16 c

17 c

18 c

19 c 20 c

21 c 22 c 23 c 24 c 25 c 26 c 27

28 c 29

30

31 c 32

33

34 c

35

36

37

38 c

39 c 40 c

41

42

43

44

SUBROUTINE ORDER

SUBROUT I NE ORDER( TEMP , I ARRAY, H)

ORDERS TEMPS IN INCREASING VALUE-ORDERS ARRAY I ARRAY ACCORDINGLY

INPUT VARIABLES:

TEMP AN ARRAY CONTAINING THE VALUES TO BE SORTED.

I ARRAY STARTS OUT CONTAINING ITS SUBSCRIPTS IN CORRESPONDING POSITIONS. IT IS ALTERED TO CONTAIN

THE ORIGINAL POSITIONS OF THE ELEMENTS IN TEMP

IN THEIR CORRESPONDING SORTED POSITIONS.

N THE NUMBER OF ELEMENTS IN TEMP.

OUTPUT VARIABLES:

TEMP THE VALUES IN INCREASING ORDER.

lARRAY THE ORIGINAL POSITION OF THE VALUES IN TEMP.

LOCAL VARIABLES:

AA TEMPORARY STORAGE FOR SORTED TEMP.

I A TEMPORARY STORAGE FOR SORTED lAARAY.

K THE SMALLEST UNSORTED VALUE'S POSITION.

BA THE SMALLEST UNSORTED VALUE.

DECLARE THE DIMENSIONS (ALL TYPES BY NAME).

DIMENSION TEMP(N) , lARRAY(N) ,AA(200) , IA(200)

FOR EACH PLACE.

DO 50 1=1,

N

BA= 2999.0

FIND THE SMALLEST UNSORTED VALUE.

DO 25 J=1,N

IF(BA-TEMP( J) ) 25,25,

STORE ITS LOCATION.

K=J

BA=TEMP( J)

25 CONTINUE

MOVE THE UNSORTED VALUE AND ITS LOCATION

INTO THE NEXT POSITION IN OUR TEMPORARY

ARRAYS.

TEMP(K) =3000.0

AA( I)=BA

IA( I) = lARRAY(K)

50 CONTINUE

C7
45: c WOVE THE VALUES INTO OUR OUTPUT ARRAYS.

46: DO 75 L=1,N

47: TEMP(L)=AA(L)

48: 1ARRAY(L) = IA(L)

49: 75 CONTINUE

50: RETURN

5 I : END

Subroutine SLYEAR (S)

1

2: C

3: C

4: c

5: C

6: C

7: C

6: C

9: c

10: c

1 1c

12: c

13: c

14: c

15: c

16: c

17: c

18: c

19

c 20 c 21

22: Q

AO • 94 • A*t •

27

c

29: c

30: c

31: c

32. c

33- c 34 c 35 c 36 c 37 c 38 c 39 c 40 c 41 c 42

43

44

45

46

47

48

49 : C

50 : C

51

52

53

54

SUBROUT I NE SLYEAR( DADB , SLDB . I CDT, LSEG , LOGUN I , I YRARR, RANKCH)

SUBROUTINE SLYEAR

A SUBROUTINE TO CREATE A FULL YEAR TAPE FROM

SELECTED PIECES OF ANOTHER WEATHER TAPE.

REQUIRES FOUR INPUT ARRAYS AND READS THE TAPE

FROM LOGICAL UNIT 10 WHICH MUST BE A DEFINE

FILE 10(3700, 220, U,N) AND WRITES IT AT LOGICAL

UNIT LOGUNI. ALSO FINDS THE AVERAGE DAILY

STANDARD DEVIATION OF EACH MONTH IN THE SELECTED

YEAR.

INPUT VARIABLES:

DADB CONTAINS THE DRY BULB AVERAGE FOR EACH YEAR

IN OUR DEFINE FILE.

SLDBT CONTAINS THE MONTHLY DRY BULB AVERAGES OF OUR

SELECTED YEAR.

ICDT CONTAINS 36 ENTRIES (12,3) WHICH ARE

THE STARTING DAYS OF OUR SELECTED SEGMENTS.

LSEG CONTAINS THE LENGTH OF EACH SEGMENT.

LOGUNI NUMBER OF THE LOGICAL UNIT ASSIGNED THE OUTPUT TAPE.

lYRARR CONTAINS THE A.D. YEAR OF THE INPUT WEATHER TAPES.

RANKCH CONTAINS THE RANKS OF THE SEGMENTS.

OUTPUT VARIABLE:

SLDSD THE DAILY STANDARD DEVIATION FOR EACH

MONTH IN THE SELECTED YEAR.

LOCAL VARIABLES:

ARRAY, I YEAR, MONTH, I DAY, I CITY

READ IN FROM THE DEFINE FILE AND WITH SOME ALTERATIONS

WRITTEN AT LOGUNI.

NDAY THE DAY OF THE MONTH IN OUR OUTPUT TAPE.

NDIS THE STARTING POINT OF A SEGMENT.

ID IS THE ENDPOINT OF THE SEGMENT.

MOWARM THE SIGNAL MONTH FOR THE WARTIUP WEEK.

NNNYR THE YEAR GIVEN THE SELECTED YEAR.

NCITY THE CITY CODE GIVEN THE WHOLE YEAR.

DECLARE THE DIMENSIONS (ALL TYPES BY NAME).

DIMENSION ARRAY(216) , ICDT( 12,3) ,LSEG( 12,3)

,

IYRARR( 10) ,RANKCH( 12,3) * ,DADB(3653) ,SLDSD( 12) ,SLDB( 12)

PRINT 600 LOGUNI

READ (5.500) NNNYR, NCITY

M0WARM=2

NCITY=L0GUNI

HNNYR= 1889+ LOGUN

I

READ IN THE FIRST WEEK AND WRITE IT OUT AS

OUR WARMUP WEEK.

DO 20 NDAY=1,7

READ( 10' NDAY) ARRAY, lYEAR, MONTH, IDAY, ICITY

WRITE (LOGUNI) ARRAY, NNNYR, MOWARM, NDAY, NC I TY

20 CONTINUE

C8
55: PRINT 605

56: C FOR EACH MONTH.

57: DO 80 M= 1 , 12 58: NDAY=0

59: SLDSD(M)=0.0

60: C FOR EACH SEGMENT.

61: DO 60 J= 1,3

62: NDAY=NDAY+1

63: NDIS=ICDT(M,J)

64: C READ THE FIRST DAY OF THE SEGMENT AND

65: C PRINT OUT ITS DATE, LENGTH, AND CITY.

66: READ( 10'NDIS) AtlRAY. I YE,\R, MONTH, IDAY, IC ITY 67 : SLDSD( M) = ( SLDB( M) -DADB( NDIS) ) *( SLDB( M) -DADB( NDIS) ) +SLDSD( M)

68: PRINT 610 M, J, IYRARR( lYEAR) .MONTH, IDAY, LSEG( M. J) , ICITY,

69: * RANKCH(M.J)

70: IDIS=NDIS+LSEG(M,J)-1

71: VRITE (LOGUNI) ARRAY, NNNYR.M, HDAY, NCI TY

72: NDIS=NDIS+1

73: C FOR EACH DAY OF THE SEGMENT READ AND

74: C WRITE THE DATA.

75: DO 40 K=NDIS, IDIS

76: NDAY=NDAY+1

77: READdO'K) ARRAY, I YEAR, MONTH, IDAY, IC ITY

78: SLDSD( M) = ( SLDB( M) -DADB( K) ) *( SLDB( M) -DADB( K) ) +SLDSD( M)

79: VRITE (LOGUKI) ARRAY, NNNYR, M, NDAY, NC ITY

80: 40 CONTINUE

81: 60 CONTINUE

82 : SLDSD( M) = SQRT( SLDSDC M) /( NDAY- 1 )

)

83: PRINT 615 M,SLDSD(H)

84: 80 CONTINUE

85: 500 FORMAT ( ) 86: 600 FORMAT (

' ENTER THE A.D. YEAR AND FIVE DIGIT CITY CODE FOR

87: * THE YEAR VRITTEN AT LOGICAL UNIT ',12)

88: 605 FORMAT (' MONTH SEGMENT YEAR MONTH DAY LENGTH CITY

89: * • RANK')

90: 610 FORMAT (2( 16) ,2X,3( 16) ,2X, I6.2X, I6,F7.2)

91: 615 FOFiMAT (' THE DAILY STANDARD DEVIATION FOR MONTH' , 14, ' IS',F8.3)

92: RETURN

93: END

Subroutine SUIJNY (A,S)

1: SUBROUTINE SUNNY ( LATD, LONG.TZN)

2: C

3: C SUBROUTINE FOR FINDING DAVN AND DAY LENGTH DATA.

4: C SEE NBS BUILDING SCIENCE SERIES 69. NBSLD, PAGE 53D.

5: C

6: DIMENSION IDYOYR( 12) 7: DATA IDYOYR /16, 44, 75, 105, 136, 166, 197,228,258,289,319,350/

8: REAL LATD, LONG, MERID, LOND

9: COMMON /DT/ IDAW( 12) , LENGTH( 12) 10: C LATD= LATITUDE, DEGREES( +NORTH, -SOUTH)

11: C LONG= LONGITUDE. DEGREES(+VEST, -EAST)

12: C 1^= TIME ZONE NUMBER

13: C STANDARD TIME

14: C ATLANTIC 4

15: C EASTERN 5

16: C CENTRAL 6

17: C MOUNTAIN 7

18: C PACIFIC 8

19: C IDYOYR= DAYS(FROM START OF YEAR)

C9
20: C SRT=SUN RISE TIME (HOURS AFTER MIDNIGHT)

21: C SST=SUN SET TIME

22: C SDA=SUN DECLINATION ANGLE , DEGREES

23: C EOT= EQUATION OF TIME .HOURS

24: FI=3. 1415927

25: DO 20 1= 1 , 12 26: X=2*FI/366.*IDY0YR( I) 27: C1 = C0S(X)

20: C2=C0S(2*X)

29: C3=C0S(3*X)

30: S1 = SIN(X)

31: S2=SIN(2*X)

32: S3=SIN(3*X)

33 : SDA= . 302-22 . 93*C 1- . 229*C2- . 243*C3+3 . 85 1*S 1+ . 002*S2- . 055»S3

34 : EOT= . 4 1 97*G 1 -3 . 2265*C2- . 0903*03-7 . 35 1 *S 1 -9 . 39 1 *S2- . 336 1 *S3- . 0002

35: EOT= EOT/60.

36: MERID=15*TZN

37: LOND=LONG-MERID

38: Y=SDA*PI/180.

39: yY=LATD*P 1/180.

40: HP=-TAN( Y)*TAN( YY)

41: TR= I2/PI*AC0S(HP)

42: SRT= ( 12-TR) -EOT+LOND/ 15

.

43: IDAWN( I) = (SRT+.5)

44: SST=SRT+2.*TR

45 : LENGTHC I

) = ( SST+ . 5)

IDAWHi I

)

46: 20 CONTINUE

47: RETURN

48: END

Subroutine WETBLB (A)

1: SUBROUTINE VETBLB (DB.DP.FB.TO)

2: C

3: C SEE NBS BUILDING SCIENCE SERIES 69. NBSLD. PAGE 33D.

4: C

5: C THIS SUBROUTINE CALCULATES THE FOLLOWINGS WEEN DRY-BULB TEMPERATURE

6: C (DB) .DEW-POINT TEMPERATURE( DP) . AND BAROMETRIC PRESSURE(PB) ARE GIVEN

7: C WB VET-BULB TEMPERATURE

8: IF (DP-DB) 20.

,

9: DP=DB

10: 20 PV=PVSF(DP)

11: PVS=PVSF{DB)

12: W=0.622*PV/(PB-PV)

13: H=0.24*DB+( ie61+0.444*DB)*V

14: IF (H) , .40

15: VB=DP

16: RETURN

17: C

18: 40 WB=VBF(H.PB)

19: RETURN

20: C

21: END

CIO
Function DPF (A,S)

1: 2: c

3: c

4: c

5: c

6: c

7:

8: 10

9: c

16: 20

1 1

12:

13:

14: 15: C

16: 30

17: 40

18: C

19:

FUNCTION DPF (PV)

SEE NBS BUILDING SCIENCE SERIES 69. NBSLD. PAGE 27D.

THIS SUBROUTINE CALCULATES DEW-POINT TEMPERATURE FOR GIVEN VAPOR PESSURE.

ryjn. IF (PV) 10. 10,20

GO TO 40

CONTINUE

Y=LOG(PV)

IF (PV.GT.0. 1836) GO TO 30

DPF=7 1 . 98+24 . 873*Y+0 . 8927*Y*Y

GO TO 40

DPF=79 . 047+30. 579*Y+ 1 , 8893*Y*Y

RETURN

END

FUNCTION PVSF (A)

1: FUNCTION PVSF(X)

2: G

3: C SEE NBS BUILDING SCIENCE SERIES 69, NBSLD. PAGE 33D.

4: C

5: C FUNCTION PSYCHROMETRIC SUBROUTINE FOR CALCULATION OF

6: C PARTIAL VAPOUR PRESSURE OF WATER AT A GIVEN TEMPERATURE

7: G FOR A SATURATED CONDITION.

8* G

9: C INPUTS X, A TEMPERATURE. DEC F. , AND RETURNS PVSF. THE SATURATED

10: C PARTIAL VAPOR PRESSURE OF WATER IN MOIST AIR, INCHES OF MERCURY.

11: C

12: DIMENSION A( 6) /-7 . 90298, 5 . 02808, - 1 . 3816E-7 , 1 1 . 344,

13: 2 8. 1328E-3.-3.49149/ . B( 4) /-9 . 097 18. -3 . 56654. 0. 876793 . 0 . 0060273/

14: 3 .P(4)

15: T=(X+459.688>/l .8

16: IF(T.LT.273. 16) GOTO 10

17: Z=373.16/T

18: P( 1)=A( 1)*(Z-1)

19: P(2)=A(2)*LOG10(Z)

20: Z1=A(4)*( 1-1/Z)

21: P(3)=A(3)*( 10**Z1-1)

22: Z1=A(6)*(Z-1)

23: P(4)=A(5)*( 10**Z1-1)

24: GOTO 20

25: 10 Z=273.16/T

26: P( 1)=B( 1)*(Z-1)

27: P(2)=B(2)*LOG10(Z)

28: P(3)=B(3)*( 1-1/Z)

29: P(4)=LOG10(B(4)

)

30: 20 SUM=0.

31: DO 30 1=1.4

32: SUM=SUM+P(I)

33: 30 CONTINUE

34: PVSF=29.921*10**SUM

35: RETURN

36: END

Cll
Function PVSF (S)

1 C FUNCTION PSYCHROWETRIC SUBROUTINE FOR CALCULATION OF

2 C PARTIAL VAPOUR PRESSURE OF WATER AT A GIVEN TEMPERATURE

3 C FOR A SATURATED CONDITION. SEE NBS BUILDING SCIENCE

4 C SERIES 69, NBSLD, PAGE 156A.

5 C

6 C INPUTS X, A TEMPERATURE, AND RETURNS PVSF, THE SATURATED PARTIAL VAPOR

7 C PRESSURE OF VATER IN MOIST AIR. 8 C X: IN DEC CELSIUS.

9 C PVSF: IN KILOPASCAL.

10 C

11 FUNCTION PVSF(X)

12 DIMENSION A(6) /'-7. 90298,5. 02808, -1 .3816E-7, 1 1 .344,

13 2 8. 1328E-3,-3.49149/ , B( 4) /-9 . 097 18 , -3 . 56654 , 0 . 876793

,

0.0060273/

14 3 ,P(4)

15 T=X+273. 16

16 IF(T.LT.273. 16) GOTO 10

17 Z=373. 16/T

18 P( 1)=A( 1)*(Z-1)

19 P(2)=A(2)*LOG10(Z)

20 Z1=A(4)*( 1-1/Z)

21 P(3)=A(3)*( 10**Z1-1)

22 Z1 = A(6)*(Z-1)

23 P(4)=A(5)*( 10**Z1-1)

24 GOTO 20

25 10 Z=273.16/'T

26 P( 1)=B( 1)*(Z-1)

27 P(2)=B(2)*LOG10(Z)

28 P(3)=B(3)*( 1-1/Z)

29 P(4)=LOG10{B(4))

30 20 SUM=0.

31 DO 30 1=1,4

32 SUM=SUM+P( I) 33 30 CONTINUE

34 PVSF= 101. 1*10«*SUM

35 RETURN

36 END

Function T^F (A)

1 FUNCTION VBF (H,PB)

2 C

3 C SEE NDS BUILDING SCIENCE SERIES 69, NBSLD, PAGE 57D.

4 C

5 C THIS PROGRAM APPROXIMATES THE WET-BULB TEMPERATURE WHEN

6 C ENTHALPY IS GIVEN.

7 IF (H) 30,30, 10 8 10 Y=LOG(H)

9 IF (H.GT. 1 1 .758) GO TO 20

10 WBF= 0 . 604 1 +3 . 484 1 * Y+ 1 . 360 1 *Y*Y+0 . 97307*Y*Y*Y

1

1

GO TO 100

12 C

13 20 WBF= 30 . 9 185-39 . 68200:*:Y+20 . 584 1*Y*Y- 1 . 758*Y*Y*Y

14 GO TO 100

15 C

16. 30 WBi= 150.

17! PV1 = PVSF( WBl)

18: W1=0.622*PV1/(PB-PV1)

19! Xl = 0.24*WBl + ( 1061+0.444:*WB1)*W1

20 Y1=H-X1

21 40 WB2=WB1-1

22 PV2=PVSF( WB2)

23 W2=0 . 622*PV2/( PB-PV2)

24 X2=0.24*WB2+( 1061+0.444*WB2)*W2

25 Y2=H-X2

26 IF (Y1*Y2) 90,60,50

27 50 WB1=WB2

28 Y1 = Y2

29 GO TO 40

30 C

012
31: 60 IF (YD 80,70,80

32: 70 VBF=VBI

33: GO TO 100

34: G

35: 80 ¥BF=WB2

36: GO TO 100

37: C

38: 90 Z=ABS( Y1/Y2)

39: WBF=(VB2*Z+VBl)/( 1+Z)

40: 100 RETURN

41: C

42: END

APPENDIX D: Example Run of SELECT Creating a Synthetic 1951 Philadelphia

Year from Jersey City 1949-1957 Years.

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APPENDIX E. Example run of ADJUST Creating a Synthetic 1951

Philadelphia Year from the Real New York. City

1951 Year.

Synopsis of execution

1. Link appropriate subroutines to main program

as indicated in APPENDIX B.

2. Assign input and output files to appropriate

units. Assign unit 7. to a file for summarized

year output.

(The output to unit 7. is in the same format as the summarized adjusted, or unadjusted year output without the alphanumeric headings.)

Assign unit 8. to the hourly weather data input.

Assign unit 9. to the hourly weather data output.

3. Execute program: Two runstreams are shown.

Runstream A. Creation of synthetic Philadelphia

weather year from the real New York City year.

Runstream B. Summarizing hourly weather data

from a synthetic Philadelphia on a monthly

basis.

El
RUNSTREAM A. Hourly climate adjustment.

i: ®ASG,A ADOUT.

2: > READY

3: «ASG.A imi.

4: READY

S: ®ASG,A NYADPHI.

6: READY

7: ©USE 7. , ADOUT.

a: READY

9: ©USE 8. ,NY51.

10: READY

11: ©USE 9. .NYADPHI.

12: > READY

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

©XQT ADJUST. ADJUST2

ENTER THE LATITUDE, LONGITUDE, AND THE TIME ZONE.

>39.5 75.2 5.0

ENTER THE NUMBER OF DAYS TO BE SKIPPED BEFORE THE START

OF THE 12 MONTH PERIOD BEING USED

FOLLOWED BY THE NUMBER OF DAYS IN IT

>0 365

ENTER 0 IF AN ADJUSTED YEAR IS TO BE MADE ELSE ENTER 1 >0

ENTER 0 0.0 IF BAROMETRIC PRESSURE DATA IS AVAILABLE

OTHERWISE ENTER 1 AND THE HEIGHT DIFFERENCE OF THE NEW

AND OLD LOCATION H( NEW) -H( OLD) IN FEET

>0 0.0

ENTER 0 IF USING SI UNITS (FOR INPUT OF CLIMATE

VARIABLES ONLY), ELSE ENTER 1 FOLLOWED BY THE CLOUD FLAG:

1 IF USING DAYTIME AVEPu\GES

0 IF USING FULL DAY AVERAGES.

> 1 0

ENTER 0 IF THE 24- HOUR DRY BULB AVERAGE IS TO BE

USED ENTER 1 IF THE MAX-MIN AVERAGE IS TO BE USED

(THE AVERAGE WHICH IS CHOSEN WILL BE USED IN THE

DEGREE DAY , DAILY STANDARD DEVIATION AND ALL OTHER DRY

BULB CALCULATIONS)

THEN ENTER THE BASE TEMPERATURES' TO BE USED IN THE DEGREE DAY CALCULATIONS IN DEG.F.:

FIRST HEATING, THEN COOLING.

>1 55. 75.

INPUT YEAR SUMMARY

45 AVG. MAX AVG. MIN EXT. MAX EXT. MIN D. D. HEAT D. D. COOL

46 43.645 30. 129 64 . 000 12. 000 561

.

500 .000

47 : 43.714 29. 536 65 .000 11. 000 514. 500 .000

48 : 48.290 36. 194 61 .000 25. 000 395. 500 .000

49 : 61.000 45. 833 83 .000 39. 000 95. 500 .000

50 : 71.645 54. 226 90 . 000 46. 000 1. 500 .000

51 : 77.000 62. 633 90 .000 53. 000 000 15 .500

52 : 83.710 69. 387 93 .000 63. 000 000 77 .500

53 : 82 . 226 67. 839 93 .000 60. 000 000 57 .500

54 : 76.033 61

.

167 84 .000 46. 000 4! 000 2 .500

55 : 65.677 53. 161 83 . 000 44. 000 26. 000 1 .000

56 : 51.033 37. 200 67 .000 22. 000 340. 000 .000

57 : 45.645 32. 710 64 .000 9. 000 498. 500 .000

58 : SUMMER DESIGN 0 HRS : 93. 10 HRS: 90. 50 HRS: 87. 200

59 : WINTER DESIGN 0 HRS 9. 10 HRS: 12. 50 HRS: 16. 200

HRS: 83. 500 HRS: 79.

HRS: 22. 500 HRS: 28.

E2
ENTER 0 IF USING VET BULB ONLY, AS -FROM AIR FORCE MANUAL 88/29

ENTER 1 IF USING DEWPOINT ONLY. AS FROM NOAA CLIMATIC ATLAS

ENTER 2 IF USING BOTH

>2

ENTER THE VEATHER AVERAGES TO ADJUST THE TAPE.

EVEN IF VALUES CANNOT BE FOUND OR ARE NOT USED

SOMETHING MUST BE ENTERED FOR ALL 96 VALUES

( ENIER ONLY ONE TYPE OF DRY BULB AVERAGE.)

IF NECESSARY ECHO THE VALUES FROM THE INPUT YEAR

MONTH DRY BULB

MAXMIN

DEG.F.

1 36 . 89

36.01

2 36.62

36.06

3 42.24

43.00

4 53.42

53.56

5 62.94

63.56

6 69 . 82

70.85

7 76.55

76.45

8 75 . 03

74.63

9 68.60

68. 19 10 59.42

59.78

11 44.12

42.38

12 39.18

38.44

ADJUSTED YEAR

CLOUD WIND DEW BAROMETRIC DAILY

COVER Speed point pressure s.d.

TENTHS KNOTS DEG.F.

6.09 14.48 25.55

6.33 8.79 26.47

6.00

,79 9,

6.33

15.38 24.37

38 24.52 30

16.60 30.24

IN.HG.

30.03

30.08 7.66

30.

16

11 9.75

29.97

6.33 10.88 30.87 30.00 6.16

5.72

,07 9,

6.21

12.87 38.77 29.86

54

11. 39 . 27 29 . 90 6 . 2779 46.57 29.83

6.14 8.25 48.39 29.87

10.55 58. 10

DEG.F

8.56

15.52

9.58

16.04

5.80

15.23

5.89

19.63

5.98

20.35

5.92

17. 13

3.69

17.65

4.29

18.55

5.85

20. 13

6.50

16.52

8.46

16.80

5.80 12.50 26.81 30.04 11.60

01 8.85 28.65 30.08 11.91 15.26

6.38

.67 7,

6.02

.95 6. 5. 17 ,35 6. 5.01

,97 6. 5.94

38

9. 99

9. 10

11. 85

5.64

29.91

60.74 29.93 6.28

.70 62.75 29.93

65.29 29.96 3.18

.86 62.82 29.93

63.67 29.95 4.49

.31 55.40 30.01

56 . 26 30 . 03 5 . 52

13.36 47.86 30.07

28 8.08 49.46 30.09 7.09

5.49 13.65 30. 18 30.04

5 . 83 9 . 24 3 1 . 37 30 . 08 9 . 09

DAILY WET

RANGE BULB

DEG.F. DEG.F.

13.52 33.00

32.66

14.18 32.32

32. 19 12.10 37.45

38.26

15.17 46.09

46.70

17.42 53.98

55.37

14.37 62.47

64.42

14.32 67.47

69.04

14.39 66.93

67.52

14.87 60.77

61. 15 12.52 53.22

54. 11 13.83 38.58

38. 14 12.94 34.97

35. 11 COMPLETE.

ADJUSTED YEAR SUMMARY

101: AVG. MAX AVG. MIN EXT. MAX EXT. MIN D.D. HEAT D. D. COOL

102: 43 . 770 28.250 62 . 489 1 1 . 838 588.690 .000

103: 44 . 080 28.040 65.648 9. 155 530.320 .000

104: 50.615 35 . 385 63.774 23.588 372 . 486 .000

105: 63.375 43.745 86 . 280 36.339 96.736 .000

106 73.735 53.385 91.427 45.643 .000 .000

107: 79.415 62 . 285 92.883 51.993 .000 26 .461

108' 85 . 275 67.625 94.023 61.931 .000 69 .255

109. 83.905 65.355 94.982 57. 164 .000 53 .729

lie. 78.255 58. 125 85 . 948 43.857 3.079 1 . 121

111. 68.040 51.520 86 . 857 41.375 28.718 2 .855

112 50 . 780 33.980 68.009 17.724 389 . 804 .000

113 46.070 30.810 64.920 6.465 520.606 .000

114 SUMMER DESIGN 0 HRS : 95. 10 HRS: 92. 50 HRS: 88. 200

115 WINTER DESIGN 0 HRS : 6. 10 HRS: 10. 50 HRS: 15. 200

HRS:

HRS:

85,

21.

500

500

HRS:

HRS:

80.

27.

E3
RUNSTREAM B. Summary of hourly data.

l: ©ASG.A ADODT.

2: >READY

3: ®ASG,A NYADPHI.

4: >READY

5: ®ASG,A RYOUT.

6: >READY

7: ®USE 7. .ADOUT.

8: READY

9: ©USE 8. .NYADPHI.

10: READY

11: ©USE 9. ,NYOUT.

12: READY

©XOT ADJUST. ADJUST2

ENTER THE LATITUDE, LONGITUDE, AND THE TIME ZONE.

>39.5 75.2 5.0

ENTER THE NUMBER OF DAYS TO BE SKIPPED BEFORE THE START

OF THE 12 MONTH PERIOD BEING USED

FOLLOWED BY THE NUMBER OF DAYS IN IT >0 365

ENTER 0 IF AN ADJUSTED YEAR IS TO BE MADE ELSE ENTER 1 > 1ENTER 0 IF THE 24-HOUR DRY BULB AVERAGE IS TO BE

USED ENTER 1 IF THE MAX-MIN AVERAGE IS TO BE USED

(THE AVERAGE WHICH IS CHOSEN WILL BE USED IN THE

DEGREE DAY , DAILY STANDARD DEVIATION AND ALL OTHER DRY

BULB CALCULATIONS)

THEN ENTER THE BASE TEMPERATURES' TO BE USED IN THE DEGREE DAY CALCULATIONS IN DEG.F.:

FIRST HEATING, THEN COOLING.

>0 55. 75.

INPUT YEAR SUMMARY

AVG. MAX AVG. MIN EXT. MAX EXT. MIN

43.770

44 . 080

50.615

63.375

73.735

79.415

85 . 275

83.905

78.255

68.040

50.780

46.070

28.250

28.040

35 . 385

43.745

53.385

62.285

67.625

65.355

58. 125

5 1 . 520

33 . 980

30.810

SUMMER DESIGN 0 HRS

WINTER DESIGN 0 HRS

MONTH

12

3

4

5

6

7

8

9

10

1

1

12

DRY BULB

24-HR

DEG.F.

35.99

35.45

42.55

52.51

62.67

70.35

75.72

73.47

67.53

59.34

41 .86

38.28

62.489

65.648

63.774

86 . 280

9 1 . 427

92 . 883

94.023

94.982

85.948

86 . 857

68.009

64.920

95.

6.

CLOUD

COVER

TENTHS

6.27

85

32

97

17

67

95

36

95

25

79

02

1 1 . 838

9. 155

23.588

36.339

45.643

51.993

61 .931

57. 164

43 . 857

4 1 . 375

17.724

6.465

10 HRS: 92

10 HRS: 10

WIND DEW

SPEED POINT

KNOTS DEG.F

8.79 26.47

9.38 24.40

10.88 30.81

9.54 39.06

8.25 48.20

7.38 60.61

6.99 65.04

6. 10 63.31

6.85 56.05

8.08 49.34

9.24 31 . 18

8.85 28.62

D. HEAT

589.253

547.455

386.218

108.879

1.786

.000

.000

.000

2.669

27.923

407.641

527.217

, 50 HRS:

, 50 HRS:

D.D COOL

.000

.000

.000

. 000

. 100

25 . 937

47.904

27.851

.000

.390

.000

. 000

88. 200 HRS:

15. 200 HRS:

BAROMETRIC DAILY DAILY

PRESSURE

IN.HG.

30.08

30. 16 30.00

29.90

29.87

29.93

29.96

29.95

30.03

30.09

30.08

30.08

S.D.

DEG.F.

7.60

9.72

06

59

59

43

87

99

05

6.80

9.54

12. 15 SUMMARIZED YEAR COMPLETE.

RANGE

DEG.F.

15.52

16.04

15.23

19.63

20.35

17. 13 17.65

18.55

20. 13 16.52

16.80

15.26

85.

21.

WET

BULB

DEG.F

32.66

32.05

38. 19 46.45

55. 17 64.29

68.79

67. 15 60.94

54.00

37.94

35.08

500 HRS:

500 HRS:

86.

27.

E4
FEDERAL INFORMATION PROCESSING STANDARD SOFTWARE SUMMARY

01. Summary date

Yr. Mo. Day

04. Software date

Yr. Mo. Day 7 9

02. Summary prepared by (Name and Phone) Edward Arens (301) 921-3595

05. Software title Fortran V Programs: SELECT and ADJUST

06. Short title

03. Summary action New Replacement Deletion B

Previous Internal Software ID

07. Internal Software ID

08. Software type

Automated Data S

System Computer Program

(~) Sut>routine/Module

09. Processing mode

Interactive or

Batch

I I Combination

10. General

Application area

Computer Systems Q Support/Utility

[HI Scientific/Engineering Q Bibliographic/Textual

Management/

Business

Process Control Other

Specific Heating & Cooling

II. Submitting organization and address Environmental Design Research Division

Center for Building Technology

National Engineering Laboratory

National Bureau of Standards

12. Technical contact(s) and phone Edward Arens

Architectural Research Group

(301) 921-3595

13. Narrative Two interactive programs, plus 12 subroutines, create composite and synthetic year-long

hourly weather data tapes for building loads prediction programs, using Weather Bureau

tapes as source data. The programs provide the user the capability for producing

'typical' weather years representative of a long-term record, and also 'extrapolated'

weather years representative of locations for which hourly data are not available. The

programs can be used individually or in sequence.

14. Keywords Climate, Data, Buildings, Energy Prediction, Loads.

15. Computer manufr and model

1108 UNIVAC

16. Computer operating system 1100 VER 33R3-RSI17. Programing language(s) FORTRAN V18. Number of source program state- ments 1000, 500

19. Computer memory requirements lOOK

20. Tape drives None

21. Disk/ Drum units L 6. S tracks, ingu|^^^22. Terminals 132 character/line

23. Other operational requirements

24. Software availability 25. Documentation availability

Available Umited In-house only Available Inadequate In-house only m

26. FOR SUBMITTING ORGANIZATION USE

185-101 Standard Form 185

1974 July

U.S. Dept. of Commsrce—NBS

(FIPS, Pub. 30)
NBS.114A (REV. 9-78)

U.S. DEPT. OF COMM. BIBLIOGRAPHIC DATA

SHEET

1. rUbLILAilON OK KtrUKI IMU. NBS BSS 1262.Gov*t. Accession Now

a5,5a.,ss^s. A^s-sxv. A\s^.\..^^ . 4. TITLE AND SUBTITLE

Geographical Exi

Data for Energy

trapolation of Typical Hourl

Calculation in Buildings

.y Weather

5. Publication Date August 1980

Pierformiftg Otpnization Cktde

Edward A. Arens, Larry Eo Flynn, Daniel N, Nail, and

Kalev Ruberg

8. Performing Organ. Report No.

9. PERFORMING ORGANIZATION NAME AND ADDRESS

NATIONAL BUREAU OF STANDARDS

DEPARTMENT OF COMMERCE

WASHINGTON, DC 20234

10. Pro|8ci/task/Work Uuit No.

11 Pnntrart/rirant Nn

12. SPONSORING ORGANIZATION NAME AND COMPLETE ADDRESS rsrree*. City, state, ziP)

Same

13. Type of Report & Period Covered Final

14. SponsWtng Agency Code

15. SUPPLEMENTARY NOTES

Library of Congress Catalog Number: 80-600059

1 Document describes a computer program; SF-185, FIPS Software Summary, is attached.

16. ABSTRACT (A 200-word or less tactual summary of moat significant information. If document includes a significant bibliograpliy or

literature survey, mention it here,)

Two techniques are developed and tested for creating composite and synthetic hourly

weather data for a wide range of sites. The first technique selects real weather

data segments from a source multiyear weather record, and links them into a com- posite synthetic year, in which the hourly values are unchanged from the source. The second technique adjusts the real hourly data values of the source to create a more completely synthetic year. The techniques may be applied individually or in

combination. The resulting synthetic year or years can be used to provide data

that is representative of long-term climate for building energy prediction either

at the first-order station where the source hourly weather data were recorded, or at a nearby second-order station for which only summarized climate averages are

available. Additionally, the adjustment technique can generate synthetic data to

represent specific time periods at second-order stations for use in energy audits

and experiments. The effectiveness of extrapolating weather data from one location

to another is assessed, and the uses of the two techniques are described. The

user-interactive Fortran programs, SELECT and ADJUST are appended.

17. KEY WORDS (six to twelve entries; alphabetical order; capitalize only the first letter of the first key word unless a proper name;

separated by semicolons) Building energy; computerized climate data.

18. AVAILABILITY [Xl Unlimited

1 1 For Official Distribution. Do Not Release to NTIS

lx~l Order From Sup. of Doc, U.S. Government Printing Office, Washington, DC

2C402

Order From National Technical Information Service (NTIS), Springfield, VA. 22161

19. SECURITY CLASS

(THIS REPORT)

UNCLASSIFIED

21. NO. OF

PRINTED PAGES

121

20. SECURITY CLASS

(THIS PAGE)

UNCLASSIFIED

22. Price

$4.50

USCOMM-DC

•ftU.S. GOVERNMENT PRINTING OFFICEi 1980-311-046/192






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